A disease diagnosis tool uses a vision transformer to determine a diagnosis of a disease from a high-resolution image of a body part. A disease diagnosis tool receives a high-resolution image of a body part. The disease diagnosis tool divides the high-resolution image into a plurality of tiles. For each tile, the disease diagnosis tool generates an embedding having a position encoding corresponding to the tile's position in the high-resolution image. The disease diagnosis tool inputs the embeddings for the tiles into a linear projection model whose output feeds a transformer and receives, as output from a model comprising the transformer, a diagnosis of a disease of the body part.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
A disease diagnosis tool uses a vision transformer to determine a diagnosis of a disease from a high-resolution image of a body part. A disease diagnosis tool receives a high-resolution image of a body part. The disease diagnosis tool divides the high-resolution image into a plurality of tiles. For each tile, the disease diagnosis tool generates an embedding having a position encoding corresponding to the tile's position in the high-resolution image. The disease diagnosis tool inputs the embeddings for the tiles into a linear projection model whose output feeds a transformer and receives, as output from a model comprising the transformer, a diagnosis of a disease of the body part.
A disease diagnosis tool validates a diagnosis of a patient. The disease diagnosis tool receives a high-resolution image of a body part. The disease diagnosis tool divides the high-resolution image into a plurality of tiles and inputs a representation of each tile into an encoder portion of a model configured to perform a disease diagnosis based on the representations. The encoder has an attention mechanism. The disease diagnosis tool obtains a plurality of tokens representative of an attention of the encoder based on the attention mechanism. Each token is associated with a position of a tile of the plurality of tiles. The disease diagnosis tool generates a heat map corresponding to the image of the body part. The heat map comprises a two-dimensional image having pixels corresponding to tiles each with an amplitude based on a level of attention represented in the plurality of tokens corresponding to the tiles.
G16H 30/40 - ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
4.
Parallel Optical Coherence Tomography Apparatuses, Systems, and Related Methods
SNAPSHOT SPECTRAL DOMAIN OPTICAL COHERENCE TOMOGRAPHER Provided is a snapshot spectral domain optical coherence tomographer comprising a light source providing a plurality of beamlets; a beam splitter, splitting the plurality of beamlets into a reference arm and a sample arm; a first optical system that projects the sample arm onto multiple locations of a sample; a second optical system for collection of a plurality of reflected sample beamlets; a third optical system projecting the reference arm to a reflecting surface and receiving a plurality of reflected reference beamlets; a parallel interferometer that provides a plurality of interferograms from each of the plurality of sample beamlets with each of the plurality of reference beamlets; an optical image mapper configured to spatially separate the plurality of interferograms; a spectrometer configured to disperse each of the interferograms into its respective spectral components and project each interferogram in parallel; and a photodetector providing photon quantification.
A61B 3/10 - Objective types, i.e. instruments for examining the eyes independent of the patients perceptions or reactions
A61B 3/00 - Apparatus for testing the eyesInstruments for examining the eyes
A61B 3/12 - Objective types, i.e. instruments for examining the eyes independent of the patients perceptions or reactions for looking at the eye fundus, e.g. ophthalmoscopes
A61B 3/14 - Arrangements specially adapted for eye photography
A device is disclosed for diagnosing a dental condition. The device captures image data representative of a tooth of a patient based on data obtained from a hardware device that scans the tooth. The device inputs the image data into a first supervised machine learning model, and receives, as output from the first supervised machine learning model, a plurality of biomarkers, each biomarker corresponding to a different location of the tooth. The device inputs the plurality of biomarkers into a second supervised machine learning model, and receives, as output from the second supervised machine learning model, a diagnosis of a dental condition.
Systems and methods are disclosed herein for classifying one or more disease conditions. In some embodiments, an application stores a common extraction model, the common extraction model trained using training examples for a plurality of diseases. The application stores a plurality of disease classifiers, each disease classifier configured to output whether or not its respective disease is present, each disease classifier trained using training examples for its respective disease. The application receives a selection of a disease and selects a disease classifier from the plurality of disease classifiers corresponding to the disease. The application inputs an image into the common extraction model and receives, as output from the common extraction model, a set of biomarkers extracted from the image. The application inputs the set of biomarkers into the selected disease classifier, the selected disease classifier configured to output whether or not the disease is present in the image.
G16H 30/40 - ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
G06V 10/70 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 10/77 - Processing image or video features in feature spacesArrangements for image or video recognition or understanding using pattern recognition or machine learning using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]Blind source separation
8.
TRAINING MANIFOLD FOUNDATIONAL MACHINE-LEARNING MODEL FOR CLASSIFYING ADDITIONAL DISEASE STATES WITH LIMITED TRAINING DATA
Systems and methods are disclosed herein for training a manifold foundational model to autonomously diagnose a new disease classification. In some embodiments, an application receives training data for a plurality of diseases, the training data for each disease including a training examples, each example having an image of a patient, a set of biomarkers indicative of a disease condition depicted in the image, and a label indicating whether the patient has the disease condition. The application trains a common extraction model using the training data for the plurality of diseases, where the common extraction model is configured to take images as input and to output biomarkers, and trains a plurality of disease classifiers, each disease classifier configured to take the output of the common extraction model as input and to output whether a respective disease for which the disease classifier is trained to detect is present in the images.
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 10/77 - Processing image or video features in feature spacesArrangements for image or video recognition or understanding using pattern recognition or machine learning using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]Blind source separation
G16H 30/40 - ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
9.
DEPLOYING MANIFOLD FOUNDATIONAL MACHINE-LEARNING MODEL FOR CLASSIFYING ADDITIONAL DISEASE STATES WITH LIMITED TRAINING DATA
Systems and methods are disclosed herein for classifying one or more disease conditions. In some embodiments, an application stores a common extraction model, the common extraction model trained using training examples for a plurality of diseases. The application stores a plurality of disease classifiers, each disease classifier configured to output whether or not its respective disease is present, each disease classifier trained using training examples for its respective disease. The application receives a selection of a disease and selects a disease classifier from the plurality of disease classifiers corresponding to the disease. The application inputs an image into the common extraction model and receives, as output from the common extraction model, a set of biomarkers extracted from the image. The application inputs the set of biomarkers into the selected disease classifier, the selected disease classifier configured to output whether or not the disease is present in the image.
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
G16H 50/50 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 30/00 - ICT specially adapted for the handling or processing of medical images
10.
USING A SET OF MACHINE LEARNING DIAGNOSTIC MODELS TO DETERMINE A DIAGNOSIS BASED ON A SKIN TONE OF A PATIENT
Systems and methods are disclosed herein for determining a diagnosis based on a base skin tone of a patient. In an embodiment, the system receives a base skin tone image of a patient, generates a calibrated base skin tone image by calibrating the base skin tone image using a reference calibration profile, and determines a base skin tone of the patient based on the calibrated base skin tone image. The system receives a concern image of a portion of the patient's skin, and selects a set of machine learning diagnostic models from a plurality of sets of candidate machine learning diagnostic models based on the base skin tone of the patient, each of the sets of candidate machine learning diagnostic models trained to receive the concern image and output a diagnosis of a condition of the patient.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
A61B 5/00 - Measuring for diagnostic purposes Identification of persons
A61B 5/103 - Measuring devices for testing the shape, pattern, size or movement of the body or parts thereof, for diagnostic purposes
G06T 7/90 - Determination of colour characteristics
G06V 10/56 - Extraction of image or video features relating to colour
G06V 10/72 - Data preparation, e.g. statistical preprocessing of image or video features
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 40/10 - Human or animal bodies, e.g. vehicle occupants or pedestriansBody parts, e.g. hands
A fully autonomous system is used to subclassify a disease in a patient. For example, a tool receives one or more images of a body part of a patient, inputs the one or more images into a diagnostic model, and receives, as output from the diagnostic model, a diagnosis for the patient. The tool determines whether the diagnosis is positive for a given disease, and, responsive to determining that the diagnosis is positive, inputs a representation of the one or more images into a diagnosis subclassification model. The tool determines, based on output from the diagnosis subclassification model, a subclassification for the diagnosis, and outputs a control signal based on the subclassification.
G16H 10/20 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 20/10 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
G16H 80/00 - ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring
14.
ARTIFICIAL INTELLIGENCE ENABLED SUB-CLASSIFICATIONS OF DISEASE STATES
A fully autonomous system is used to subclassify a disease in a patient. For example, a tool receives one or more images of a body part of a patient, inputs the one or more images into a diagnostic model, and receives, as output from the diagnostic model, a diagnosis for the patient. The tool determines whether the diagnosis is positive for a given disease, and, responsive to determining that the diagnosis is positive, inputs a representation of the one or more images into a diagnosis subclassification model. The tool determines, based on output from the diagnosis subclassification model, a subclassification for the diagnosis, and outputs a control signal based on the subclassification.
A system for recording, storing and processing diagnostic information, including: a computer implementing a computer-readable media including digital data and ground truth; a registry constructed and arranged to store and associate transactions or accesses on the data; and a machine learning system that considers each learning step modification a microtransaction for the data used in that step and which is recorded in the transaction registry. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.
G16H 30/40 - ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
16.
IMAGE RETENTION AND STITCHING FOR MINIMAL-FLASH EYE DISEASE DIAGNOSIS
Systems and methods are provided herein for minimizing retinal exposure to flash during image gathering for diagnosis. In an embodiment, a system captures a plurality of retinal images of different retinal regions. The system determines that a first portion of a first image does not meet a criterion while a second portion of the first image does meet the criterion, identifies a portion of the retina depicted in the first portion that does not meet the criterion, and determines whether the portion of the retina is depicted in a third portion of a second image and whether the third portion meets the criterion. Responsive to determining that the third portion meets the criterion, the system performs the diagnosis. Responsive to determining that the portion of the retina is not depicted in the second image, the system captures an additional image of the retinal region.
G16H 30/40 - ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
Disclosed is a system for qualifying medical images submitted by user for diagnostic analysis comprising: an image input module, configured to receive one or more image input by a user; an image protocol conformation module, configured to receive the one or more images from the image input module, and further configured to analyze each of the one or more images for conformity with a predefined protocol and wherein images that do not conform to the predefined protocol are flagged as non-conforming images; an image output module, configured to identify to the user each of the one or more images flagged as non-conforming and prompting the user to resubmit a new image for each of the non-conforming images; and an image resubmission module, configured to receive the user resubmitted image and provide the resubmitted image to the image protocol conformation module.
A diagnosis system trains a set of machine-learned diagnosis models that are configured to receive an image of a patient and generate predictions on whether the patient has one or more health conditions. In one embodiment, the set of machine-learned models are trained to generate predictions for images that contain two or more underlying health conditions of the patient. In one instance, the symptoms for the two or more health conditions are shown as two or more overlapping skin abnormalities on the patient. By using the architectures of the set of diagnosis models described herein, the diagnosis system can generate more accurate predictions for images that contain overlapping symptoms for two or more health conditions compared to existing systems.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 30/20 - ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS
19.
USING INFRARED TO DETECT PROPER EYE ALIGNMENT BEFORE CAPTURING RETINAL IMAGES
Systems and methods are disclosed herein for detecting eye alignment during retinal imaging. In an embodiment, the system receives an infrared stream from an imaging device, the infrared stream showing characteristics of an eye of a patient. The system determines, based on the infrared stream, that the eye is improperly aligned at a first time, and outputs sensory feedback indicative of the improper alignment. The system detects, based on the infrared stream at a second time later than the first time, that the eye is properly aligned, and receives an image of a retina of the properly aligned eye from the imaging device.
A61B 3/15 - Arrangements specially adapted for eye photography with means for aligning, spacing or blocking spurious reflection
A61B 3/113 - Objective types, i.e. instruments for examining the eyes independent of the patients perceptions or reactions for determining or recording eye movement
G16H 40/63 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
20.
Monitoring Surface Cleaning of Medical Surfaces Using Video Streaming
A cleaning wizard monitors and provides feedback for cleaning of medical equipment to ensure that cleaning is performed based on best practices. The cleaning wizard receives a video stream comprising an item of medical equipment and inputs a first set of video frames from the video stream into a first machine learning model. The first machine learning model is trained to output whether the first set of video frames corresponds to activity that initiates a cleaning protocol for the item of medical equipment. Responsive to the cleaning protocol being initiated, the cleaning wizard inputs a second set of video frames into a second machine learning model trained to output whether the second set of frames meets criteria of the cleaning protocol. Responsive to all criteria of the cleaning protocol being met, the cleaning wizard transmits a notification to an operator that the cleaning protocol is complete.
G06V 20/40 - ScenesScene-specific elements in video content
A61L 2/24 - Apparatus using programmed or automatic operation
A61L 2/28 - Devices for testing the effectiveness or completeness of sterilisation, e.g. indicators which change colour
G06T 7/70 - Determining position or orientation of objects or cameras
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G16H 40/20 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
G16H 40/67 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
21.
DIRECT MEDICAL TREATMENT PREDICTIONS USING ARTIFICIAL INTELLIGENCE
A device is disclosed herein that receives image data corresponding to an anatomy of a patient. The device applies the image data to one or more feature models trained using training data that pairs anatomical images to an anatomical feature label, and receives, as output from the one or more feature models, scores for each of a plurality of anatomical features corresponding to the image data. The device applies the scores as input to a treatment model, the treatment model trained to output a prediction of a measure of efficacy of a particular treatment based on features of the patient's anatomy. The device receives, as output from the treatment model, data representative of the predicted measure of efficacy of the particular treatment.
G16H 20/00 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
G06F 18/214 - Generating training patternsBootstrap methods, e.g. bagging or boosting
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
22.
Parallel optical coherence tomography apparatuses, systems, and related methods
Provided is a snapshot spectral domain optical coherence tomographer comprising a light source providing a plurality of beamlets; a beam splitter, splitting the plurality of beamlets into a reference arm and a sample arm; a first optical system that projects the sample arm onto multiple locations of a sample; a second optical system for collection of a plurality of reflected sample beamlets; a third optical system projecting the reference arm to a reflecting surface and receiving a plurality of reflected reference beamlets; a parallel interferometer that provides a plurality of interferograms from each of the plurality of sample beamlets with each of the plurality of reference beamlets; an optical image mapper configured to spatially separate the plurality of interferograms; a spectrometer configured to disperse each of the interferograms into its respective spectral components and project each interferogram in parallel; and a photodetector providing photon quantification.
A61B 3/10 - Objective types, i.e. instruments for examining the eyes independent of the patients perceptions or reactions
A61B 3/00 - Apparatus for testing the eyesInstruments for examining the eyes
A61B 3/12 - Objective types, i.e. instruments for examining the eyes independent of the patients perceptions or reactions for looking at the eye fundus, e.g. ophthalmoscopes
A61B 3/14 - Arrangements specially adapted for eye photography
Systems and methods are disclosed herein for determining a diagnosis based on a base skin tone of a patient. In an embodiment, the system receives a base skin tone image of a patient, generates a calibrated base skin tone image by calibrating the base skin tone image using a reference calibration profile, and determines a base skin tone of the patient based on the calibrated base skin tone image. The system receives a concern image of a portion of the patient's skin, and selects a set of machine learning diagnostic models from a plurality of sets of candidate machine learning diagnostic models based on the base skin tone of the patient, each of the sets of candidate machine learning diagnostic models trained to receive the concern image and output a diagnosis of a condition of the patient.
G06V 10/56 - Extraction of image or video features relating to colour
G06V 10/72 - Data preparation, e.g. statistical preprocessing of image or video features
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 40/10 - Human or animal bodies, e.g. vehicle occupants or pedestriansBody parts, e.g. hands
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
24.
AUTONOMOUS DIAGNOSIS OF EAR DISEASES FROM BIOMARKER DATA
A fully autonomous system is used to diagnose an ear infection in a patient. For example, a processor receives patient data about a patient, the patient data comprising at least one of: patient history from medical records for the patient, one or more vitals measurements of the patient, and answers from the patient about the patient's condition. The processor receives a set of biomarker features extracted from measurement data taken from an ear of the patient. The processor synthesizes the patient data and the biomarker features into input data, and applies the synthesized input data to a trained diagnostic model, the diagnostic model comprising a machine learning model configured to output a probability-based diagnosis of an ear infection from the synthesized input data. The processor outputs the determined diagnosis from the diagnostic model. A service may then determine a therapy for the patient based on the determined diagnosis.
A61B 1/227 - Instruments for performing medical examinations of the interior of cavities or tubes of the body by visual or photographical inspection, e.g. endoscopesIlluminating arrangements therefor for ears, i.e. otoscopes
A61B 5/00 - Measuring for diagnostic purposes Identification of persons
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
09 - Scientific and electric apparatus and instruments
41 - Education, entertainment, sporting and cultural services
44 - Medical, veterinary, hygienic and cosmetic services; agriculture, horticulture and forestry services
Goods & Services
Cameras; downloadable computer software for capturing and analyzing images of the eye to determine signs of disease for use on hand held devices; Downloadable computer software using artificial intelligence for capturing and analyzing images of the eye to determine signs of disease for use on hand held devices. Training in the use of medical analysis software to determine signs of disease by capturing and analyzing images of the eye. Medical analysis of the eye for the diagnosis of disease in persons; Medical diagnosis of diabetic retinopathy, macular edema, age related macular degeneration, stroke, myocardial infarction, cerebral atrophy, cognitive decline, and hypertension by capturing and analyzing images of the eye; Medical imaging services.
26.
Dynamic Adjustment of Flash Intensity Based on Retinal Pigmentation
Systems and methods are disclosed herein for adjusting flash intensity based on retinal pigmentation. In an embodiment, a processor determines a retinal pigmentation of a retina of an eye positioned at an imaging device. The processor commands the imaging device to adjust an intensity of a flash component from a first intensity to a second intensity based on the retinal pigmentation. The processor commands the imaging device to capture an image that is lit by the flash component at the second intensity, and receives the image from the imaging device.
A61B 3/14 - Arrangements specially adapted for eye photography
A61B 3/00 - Apparatus for testing the eyesInstruments for examining the eyes
A61B 3/12 - Objective types, i.e. instruments for examining the eyes independent of the patients perceptions or reactions for looking at the eye fundus, e.g. ophthalmoscopes
27.
Data aggregation, integration and analysis system and related devices and methods
A system for recording, storing and processing diagnostic information, including: a computer implementing a computer-readable media including digital data and ground truth; a registry constructed and arranged to store and associate transactions or accesses on the data; and a machine learning system that considers each learning step modification a microtransaction for the data used in that step and which is recorded in the transaction registry. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.
G16H 30/40 - ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
09 - Scientific and electric apparatus and instruments
41 - Education, entertainment, sporting and cultural services
44 - Medical, veterinary, hygienic and cosmetic services; agriculture, horticulture and forestry services
Goods & Services
(1) Cameras; downloadable computer software for capturing and analyzing images of the eye to determine signs of disease for use on hand held devices; Downloadable computer software using artificial intelligence for capturing and analyzing images of the eye to determine signs of disease for use on hand held devices (1) Training in the use of medical analysis software to determine signs of disease by capturing and analyzing images of the eye
(2) Medical analysis of the eye for the diagnosis of disease in persons; Medical diagnosis of diabetic retinopathy, macular edema, age related macular degeneration, stroke, myocardial infarction, cerebral atrophy, cognitive decline, and hypertension by capturing and analyzing images of the eye; Medical imaging services
29.
Systems and methods for alignment of the eye for ocular imaging
An ocular alignment system for aligning a subject's eye with an optical axis of an ocular imaging device comprising one or more guide light and one or more baffle configured to mask the one or more guide light from view of the subject such that the one or more guide light is only visible to the subject when the eye of the subject is aligned with the optical axis of an ocular imaging system.
A61B 3/00 - Apparatus for testing the eyesInstruments for examining the eyes
A61B 3/12 - Objective types, i.e. instruments for examining the eyes independent of the patients perceptions or reactions for looking at the eye fundus, e.g. ophthalmoscopes
A61B 3/15 - Arrangements specially adapted for eye photography with means for aligning, spacing or blocking spurious reflection
30.
Image retention and stitching for minimal-flash eye disease diagnosis
Systems and methods are provided herein for minimizing retinal exposure to flash during image gathering for diagnosis. In an embodiment, a system captures a plurality of retinal images of different retinal regions. The system determines that a first portion of a first image does not meet a criterion while a second portion of the first image does meet the criterion, identifies a portion of the retina depicted in the first portion that does not meet the criterion, and determines whether the portion of the retina is depicted in a third portion of a second image and whether the third portion meets the criterion. Responsive to determining that the third portion meets the criterion, the system performs the diagnosis. Responsive to determining that the portion of the retina is not depicted in the second image, the system captures an additional image of the retinal region.
G16H 30/40 - ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
09 - Scientific and electric apparatus and instruments
41 - Education, entertainment, sporting and cultural services
44 - Medical, veterinary, hygienic and cosmetic services; agriculture, horticulture and forestry services
Goods & Services
Cameras; downloadable computer software for capturing and analyzing images of the eye to determine signs of disease for use on hand held devices; Downloadable computer software using artificial intelligence for capturing and analyzing images of the eye to determine signs of disease for use on hand held devices Training in the use of medical analysis software to determine signs of disease by capturing and analyzing images of the eye Medical analysis of the eye for the diagnosis of disease in persons; Medical diagnosis of diabetic retinopathy, macular edema, age related macular degeneration, stroke, myocardial infarction, cerebral atrophy, cognitive decline, and hypertension by capturing and analyzing images of the eye; Medical imaging services
34.
AUTONOMOUS DIAGNOSIS OF A DISORDER IN A PATIENT FROM IMAGE ANALYSIS
Provide are systems methods and devices for diagnosing disease in medical images. In certain aspects, disclosed is a method for training a neural network to detect features in a retinal image including the steps of: a) extracting one or more features images from a Train_0 set, a Test_0 set, a Train_1 set and a Test_1 set; b) combining and randomizing the feature images from Train_0 and Train_1 into a Training data set; c) combining and randomizing the feature images from Test_0 and Test_1 into a testing dataset; d) training a plurality of neural networks having different architectures using a subset of the training dataset while testing on a subset of the testing dataset; e) identifying the best neural network based on each of the plurality of neural networks performance on the testing data set; f) inputting images from Test_0, Train_1, Train_0 and Test_1 to the best neural network and identifying a limited number of false positives and false negative and adding the false positives and false negatives to the training dataset and testing dataset; and g) repeating steps d)-g) until an objective performance threshold is reached.
G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
G06F 18/214 - Generating training patternsBootstrap methods, e.g. bagging or boosting
09 - Scientific and electric apparatus and instruments
41 - Education, entertainment, sporting and cultural services
44 - Medical, veterinary, hygienic and cosmetic services; agriculture, horticulture and forestry services
Goods & Services
Cameras; downloadable computer software for capturing and
analyzing images of the eye to determine signs of disease;
downloadable computer software using artificial intelligence
for capturing and analyzing images of the eye to determine
signs of disease. Training in the use of medical analysis software to
determine signs of disease by capturing and analyzing images
of the eye. Medical analysis of the eye for the diagnosis of disease in
persons; medical diagnosis of diabetic retinopathy, macular
edema, age related macular degeneration, stroke, myocardial
infarction, cerebral atrophy, cognitive decline, and
hypertension by capturing and analyzing images of the eye;
medical imaging services.
36.
Autonomous diagnosis of ear diseases from biomarker data
A fully autonomous system is used to diagnose an ear infection in a patient. For example, a processor receives patient data about a patient, the patient data comprising at least one of: patient history from medical records for the patient, one or more vitals measurements of the patient, and answers from the patient about the patient's condition. The processor receives a set of biomarker features extracted from measurement data taken from an ear of the patient. The processor synthesizes the patient data and the biomarker features into input data, and applies the synthesized input data to a trained diagnostic model, the diagnostic model comprising a machine learning model configured to output a probability-based diagnosis of an ear infection from the synthesized input data. The processor outputs the determined diagnosis from the diagnostic model. A service may then determine a therapy for the patient based on the determined diagnosis.
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
A61B 5/00 - Measuring for diagnostic purposes Identification of persons
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
A61B 1/227 - Instruments for performing medical examinations of the interior of cavities or tubes of the body by visual or photographical inspection, e.g. endoscopesIlluminating arrangements therefor for ears, i.e. otoscopes
09 - Scientific and electric apparatus and instruments
41 - Education, entertainment, sporting and cultural services
44 - Medical, veterinary, hygienic and cosmetic services; agriculture, horticulture and forestry services
Goods & Services
(1) Cameras; downloadable computer software for capturing and analyzing images of the eye to determine signs of disease; downloadable computer software using artificial intelligence for capturing and analyzing images of the eye to determine signs of disease. (1) Training in the use of medical analysis software to determine signs of disease by capturing and analyzing images of the eye.
(2) Medical analysis of the eye for the diagnosis of disease in persons; medical diagnosis of diabetic retinopathy, macular edema, age related macular degeneration, stroke, myocardial infarction, cerebral atrophy, cognitive decline, and hypertension by capturing and analyzing images of the eye; medical imaging services.
09 - Scientific and electric apparatus and instruments
41 - Education, entertainment, sporting and cultural services
44 - Medical, veterinary, hygienic and cosmetic services; agriculture, horticulture and forestry services
Goods & Services
Cameras; downloadable computer software for capturing and analyzing images of the eye to determine signs of disease; Downloadable computer software using artificial intelligence for capturing and analyzing images of the eye to determine signs of disease. Training in the use of medical analysis software to determine signs of disease by capturing and analyzing images of the eye. Medical analysis of the eye for the diagnosis of disease in persons; Medical diagnosis of diabetic retinopathy, macular edema, age related macular degeneration, stroke, myocardial infarction, cerebral atrophy, cognitive decline, and hypertension by capturing and analyzing images of the eye; Medical imaging services.
09 - Scientific and electric apparatus and instruments
41 - Education, entertainment, sporting and cultural services
44 - Medical, veterinary, hygienic and cosmetic services; agriculture, horticulture and forestry services
Goods & Services
Cameras; downloadable computer software for capturing and analyzing images of the eye to determine signs of disease; Downloadable computer software using artificial intelligence for capturing and analyzing images of the eye to determine signs of disease. Training in the use of medical analysis software to determine signs of disease by capturing and analyzing images of the eye. Medical analysis of the eye for the diagnosis of disease in persons; Medical diagnosis of diabetic retinopathy, macular edema, age related macular degeneration, stroke, myocardial infarction, cerebral atrophy, cognitive decline, and hypertension by capturing and analyzing images of the eye; Medical imaging services.
09 - Scientific and electric apparatus and instruments
41 - Education, entertainment, sporting and cultural services
44 - Medical, veterinary, hygienic and cosmetic services; agriculture, horticulture and forestry services
Goods & Services
(1) Cameras; downloadable computer software for capturing and analyzing images of the eye to determine signs of disease; Downloadable computer software using artificial intelligence for capturing and analyzing images of the eye to determine signs of disease; (1) Training in the use of medical analysis software to determine signs of disease by capturing and analyzing images of the eye;
(2) Medical analysis of the eye for the diagnosis of disease in persons; Medical diagnosis of diabetic retinopathy, macular edema, age related macular degeneration, stroke, myocardial infarction, cerebral atrophy, cognitive decline, and hypertension by capturing and analyzing images of the eye; Medical imaging services;
09 - Scientific and electric apparatus and instruments
41 - Education, entertainment, sporting and cultural services
44 - Medical, veterinary, hygienic and cosmetic services; agriculture, horticulture and forestry services
Goods & Services
(1) Cameras; downloadable computer software for capturing and analyzing images of the eye to determine signs of disease; Downloadable computer software using artificial intelligence for capturing and analyzing images of the eye to determine signs of disease; (1) Training in the use of medical analysis software to determine signs of disease by capturing and analyzing images of the eye;
(2) Medical analysis of the eye for the diagnosis of disease in persons; Medical diagnosis of diabetic retinopathy, macular edema, age related macular degeneration, stroke, myocardial infarction, cerebral atrophy, cognitive decline, and hypertension by capturing and analyzing images of the eye; Medical imaging services;
42.
Data aggregation, integration and analysis system and related devices and methods
A system for recording, storing and processing diagnostic information, including: a computer implementing a computer-readable media including digital data and ground truth; a registry constructed and arranged to store and associate transactions or accesses on the data; and a machine learning system that considers each learning step modification a microtransaction for the data used in that step and which is recorded in the transaction registry. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.
G16H 30/40 - ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
42 - Scientific, technological and industrial services, research and design
Goods & Services
Non-downloadable web-based software, namely, providing temporary use of a non-downloadable web-based software for authorizing access to healthcare diagnostic data
44.
TOOTH DECAY DIAGNOSTICS USING ARTIFICIAL INTELLIGENCE
A device is disclosed for diagnosing a dental condition. The device captures image data representative of a tooth of a patient based on data obtained from a hardware device that scans the tooth. The device inputs the image data into a first supervised machine learning model, and receives, as output from the first supervised machine learning model, a plurality of biomarkers, each biomarker corresponding to a different location of the tooth. The device inputs the plurality of biomarkers into a second supervised machine learning model, and receives, as output from the second supervised machine learning model, a diagnosis of a dental condition.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
45.
Tooth decay diagnostics using artificial intelligence
A device is disclosed for diagnosing a dental condition. The device captures image data representative of a tooth of a patient based on data obtained from a hardware device that scans the tooth. The device inputs the image data into a first supervised machine learning model, and receives, as output from the first supervised machine learning model, a plurality of biomarkers, each biomarker corresponding to a different location of the tooth. The device inputs the plurality of biomarkers into a second supervised machine learning model, and receives, as output from the second supervised machine learning model, a diagnosis of a dental condition.
A device is disclosed for diagnosing a dental condition. The device captures image data representative of a tooth of a patient based on data obtained from a hardware device that scans the tooth. The device inputs the image data into a first supervised machine learning model, and receives, as output from the first supervised machine learning model, a plurality of biomarkers, each biomarker corresponding to a different location of the tooth. The device inputs the plurality of biomarkers into a second supervised machine learning model, and receives, as output from the second supervised machine learning model, a diagnosis of a dental condition.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
47.
DIRECT MEDICAL TREATMENT PREDICTIONS USING ARTIFICIAL INTELLIGENCE
A device is disclosed herein that receives image data corresponding to an anatomy of a patient. The device applies the image data to one or more feature models trained using training data that pairs anatomical images to an anatomical feature label, and receives, as output from the one or more feature models, scores for each of a plurality of anatomical features corresponding to the image data. The device applies the scores as input to a treatment model, the treatment model trained to output a prediction of a measure of efficacy of a particular treatment based on features of the patient' s anatomy. The device receives, as output from the treatment model, data representative of the predicted measure of efficacy of the particular treatment.
G16H 20/00 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
G16H 30/40 - ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
48.
Direct medical treatment predictions using artificial intelligence
A device is disclosed herein that receives image data corresponding to an anatomy of a patient. The device applies the image data to one or more feature models trained using training data that pairs anatomical images to an anatomical feature label, and receives, as output from the one or more feature models, scores for each of a plurality of anatomical features corresponding to the image data. The device applies the scores as input to a treatment model, the treatment model trained to output a prediction of a measure of efficacy of a particular treatment based on features of the patient's anatomy. The device receives, as output from the treatment model, data representative of the predicted measure of efficacy of the particular treatment.
G16H 20/00 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
G06F 18/214 - Generating training patternsBootstrap methods, e.g. bagging or boosting
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
49.
DIRECT MEDICAL TREATMENT PREDICTIONS USING ARTIFICIAL INTELLIGENCE
A device is disclosed herein that receives image data corresponding to an anatomy of a patient. The device applies the image data to one or more feature models trained using training data that pairs anatomical images to an anatomical feature label, and receives, as output from the one or more feature models, scores for each of a plurality of anatomical features corresponding to the image data. The device applies the scores as input to a treatment model, the treatment model trained to output a prediction of a measure of efficacy of a particular treatment based on features of the patient' s anatomy. The device receives, as output from the treatment model, data representative of the predicted measure of efficacy of the particular treatment.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 20/00 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
09 - Scientific and electric apparatus and instruments
41 - Education, entertainment, sporting and cultural services
44 - Medical, veterinary, hygienic and cosmetic services; agriculture, horticulture and forestry services
Goods & Services
Cameras; downloadable computer software for capturing and analyzing images of the eye to determine signs of disease; Downloadable computer software using artificial intelligence for capturing and analyzing images of the eye to determine signs of disease Training in the use of medical analysis software to determine signs of disease by capturing and analyzing images of the eye Medical analysis of the eye for the diagnosis of disease in persons; Medical diagnosis of diabetic retinopathy, macular edema, age related macular degeneration, stroke, myocardial infarction, cerebral atrophy, cognitive decline, and hypertension by capturing and analyzing images of the eye; Medical imaging services
09 - Scientific and electric apparatus and instruments
41 - Education, entertainment, sporting and cultural services
44 - Medical, veterinary, hygienic and cosmetic services; agriculture, horticulture and forestry services
Goods & Services
Cameras; downloadable computer software for capturing and analyzing images of the eye to determine signs of disease; Downloadable computer software using artificial intelligence for capturing and analyzing images of the eye to determine signs of disease Training in the use of medical analysis software to determine signs of disease by capturing and analyzing images of the eye Medical analysis of the eye for the diagnosis of disease in persons; Medical diagnosis of diabetic retinopathy, macular edema, age related macular degeneration, stroke, myocardial infarction, cerebral atrophy, cognitive decline, and hypertension by capturing and analyzing images of the eye; Medical imaging services
52.
Systems for detecting and identifying coincident conditions
A diagnosis system trains a set of machine-learned diagnosis models that are configured to receive an image of a patient and generate predictions on whether the patient has one or more health conditions. In one embodiment, the set of machine-learned models are trained to generate predictions for images that contain two or more underlying health conditions of the patient. In one instance, the symptoms for the two or more health conditions are shown as two or more overlapping skin abnormalities on the patient. By using the architectures of the set of diagnosis models described herein, the diagnosis system can generate more accurate predictions for images that contain overlapping symptoms for two or more health conditions compared to existing systems.
G16H 30/20 - ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
53.
Monitoring surface cleaning of medical surfaces using video streaming
A cleaning wizard monitors and provides feedback for cleaning of medical equipment to ensure that cleaning is performed based on best practices. The cleaning wizard receives a video stream comprising an item of medical equipment and inputs a first set of video frames from the video stream into a first machine learning model. The first machine learning model is trained to output whether the first set of video frames corresponds to activity that initiates a cleaning protocol for the item of medical equipment. Responsive to the cleaning protocol being initiated, the cleaning wizard inputs a second set of video frames into a second machine learning model trained to output whether the second set of frames meets criteria of the cleaning protocol. Responsive to all criteria of the cleaning protocol being met, the cleaning wizard transmits a notification to an operator that the cleaning protocol is complete.
G06V 20/40 - ScenesScene-specific elements in video content
A61L 2/24 - Apparatus using programmed or automatic operation
A61L 2/28 - Devices for testing the effectiveness or completeness of sterilisation, e.g. indicators which change colour
G06T 7/70 - Determining position or orientation of objects or cameras
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G16H 40/20 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
G16H 40/67 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
54.
Using a set of machine learning diagnostic models to determine a diagnosis based on a skin tone of a patient
Systems and methods are disclosed herein for determining a diagnosis based on a base skin tone of a patient. In an embodiment, the system receives a base skin tone image of a patient, generates a calibrated base skin tone image by calibrating the base skin tone image using a reference calibration profile, and determines a base skin tone of the patient based on the calibrated base skin tone image. The system receives a concern image of a portion of the patient's skin, and selects a set of machine learning diagnostic models from a plurality of sets of candidate machine learning diagnostic models based on the base skin tone of the patient, each of the sets of candidate machine learning diagnostic models trained to receive the concern image and output a diagnosis of a condition of the patient.
G06V 10/56 - Extraction of image or video features relating to colour
G06V 10/72 - Data preparation, e.g. statistical preprocessing of image or video features
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 40/10 - Human or animal bodies, e.g. vehicle occupants or pedestriansBody parts, e.g. hands
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
09 - Scientific and electric apparatus and instruments
41 - Education, entertainment, sporting and cultural services
44 - Medical, veterinary, hygienic and cosmetic services; agriculture, horticulture and forestry services
Goods & Services
Cameras; downloadable computer software for capturing and analyzing images of the eye to determine signs of disease; Downloadable computer software using artificial intelligence for capturing and analyzing images of the eye to determine signs of disease Training in the use of medical analysis software to determine signs of disease by capturing and analyzing images of the eye Medical analysis of the eye for the diagnosis of disease in persons; Medical diagnosis of diabetic retinopathy, macular edema, age related macular degeneration, stroke, myocardial infarction, cerebral atrophy, cognitive decline, and hypertension by capturing and analyzing images of the eye; Medical imaging services
56.
DIAGNOSING SKIN CONDITIONS USING MACHINE-LEARNED MODELS
A diagnosis system trains a set of machine-learned diagnosis models that are configured to receive an image of a patient and generate predictions on whether the patient has one or more health conditions. In one embodiment, the set of machine-learned models are trained to generate predictions for images that contain two or more underlying health conditions of the patient. In one instance, the symptoms for the two or more health conditions are shown as two or more overlapping skin abnormalities on the patient. By using the architectures of the set of diagnosis models described herein, the diagnosis system can generate more accurate predictions for images that contain overlapping symptoms for two or more health conditions compared to existing systems.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 30/20 - ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS
Disclosed is a system for qualifying medical images submitted by user for diagnostic analysis comprising: an image input module, configured to receive one or more image input by a user; an image protocol conformation module, configured to receive the one or more images from the image input module, and further configured to analyze each of the one or more images for conformity with a predefined protocol and wherein images that do not conform to the predefined protocol are flagged as non-conforming images; an image output module, configured to identify to the user each of the one or more images flagged as non-conforming and prompting the user to resubmit a new image for each of the non-conforming images; and an image resubmission module, configured to receive the user resubmitted image and provide the resubmitted image to the image protocol conformation module.
Systems and methods are disclosed herein for detecting eye alignment during retinal imaging. In an embodiment, the system receives an infrared stream from an imaging device, the infrared stream showing characteristics of an eye of a patient. The system determines, based on the infrared stream, that the eye is improperly aligned at a first time, and outputs sensory feedback indicative of the improper alignment. The system detects, based on the infrared stream at a second time later than the first time, that the eye is properly aligned, and receives an image of a retina of the properly aligned eye from the imaging device.
A61B 3/00 - Apparatus for testing the eyesInstruments for examining the eyes
A61B 3/02 - Subjective types, i.e. testing apparatus requiring the active assistance of the patient
A61B 3/10 - Objective types, i.e. instruments for examining the eyes independent of the patients perceptions or reactions
A61B 3/113 - Objective types, i.e. instruments for examining the eyes independent of the patients perceptions or reactions for determining or recording eye movement
A61B 3/12 - Objective types, i.e. instruments for examining the eyes independent of the patients perceptions or reactions for looking at the eye fundus, e.g. ophthalmoscopes
A61B 3/15 - Arrangements specially adapted for eye photography with means for aligning, spacing or blocking spurious reflection
G16H 40/63 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
59.
USING INFRARED TO DETECT PROPER EYE ALIGNMENT BEFORE CAPTURING RETINAL IMAGES
Systems and methods are disclosed herein for detecting eye alignment during retinal imaging. In an embodiment, the system receives an infrared stream from an imaging device, the infrared stream showing characteristics of an eye of a patient. The system determines, based on the infrared stream, that the eye is improperly aligned at a first time, and outputs sensory feedback indicative of the improper alignment. The system detects, based on the infrared stream at a second time later than the first time, that the eye is properly aligned, and receives an image of a retina of the properly aligned eye from the imaging device.
A61B 3/00 - Apparatus for testing the eyesInstruments for examining the eyes
A61B 3/10 - Objective types, i.e. instruments for examining the eyes independent of the patients perceptions or reactions
A61B 3/12 - Objective types, i.e. instruments for examining the eyes independent of the patients perceptions or reactions for looking at the eye fundus, e.g. ophthalmoscopes
A61B 3/14 - Arrangements specially adapted for eye photography
A61B 3/15 - Arrangements specially adapted for eye photography with means for aligning, spacing or blocking spurious reflection
60.
USING INFRARED TO DETECT PROPER EYE ALIGNMENT BEFORE CAPTURING RETINAL IMAGES
Systems and methods are disclosed herein for detecting eye alignment during retinal imaging. In an embodiment, the system receives an infrared stream from an imaging device, the infrared stream showing characteristics of an eye of a patient. The system determines, based on the infrared stream, that the eye is improperly aligned at a first time, and outputs sensory feedback indicative of the improper alignment. The system detects, based on the infrared stream at a second time later than the first time, that the eye is properly aligned, and receives an image of a retina of the properly aligned eye from the imaging device.
A61B 3/15 - Arrangements specially adapted for eye photography with means for aligning, spacing or blocking spurious reflection
A61B 3/00 - Apparatus for testing the eyesInstruments for examining the eyes
A61B 3/10 - Objective types, i.e. instruments for examining the eyes independent of the patients perceptions or reactions
A61B 3/12 - Objective types, i.e. instruments for examining the eyes independent of the patients perceptions or reactions for looking at the eye fundus, e.g. ophthalmoscopes
A61B 3/14 - Arrangements specially adapted for eye photography
G06K 9/46 - Extraction of features or characteristics of the image
61.
IMAGE RETENTION AND STITCHING FOR MINIMAL-FLASH EYE DISEASE DIAGNOSIS
Systems and methods are provided herein for minimizing retinal exposure to flash during image gathering for diagnosis. In an embodiment, a system captures a plurality of retinal images of different retinal regions. The system determines that a first portion of a first image does not meet a criterion while a second portion of the first image does meet the criterion, identifies a portion of the retina depicted in the first portion that does not meet the criterion, and determines whether the portion of the retina is depicted in a third portion of a second image and whether the third portion meets the criterion. Responsive to determining that the third portion meets the criterion, the system performs the diagnosis. Responsive to determining that the portion of the retina is not depicted in the second image, the system captures an additional image of the retinal region.
Systems and methods are disclosed herein for adjusting flash intensity based on retinal pigmentation. In an embodiment, a processor determines a retinal pigmentation of a retina of an eye positioned at an imaging device. The processor commands the imaging device to adjust an intensity of a flash component from a first intensity to a second intensity based on the retinal pigmentation. The processor commands the imaging device to capture an image that is lit by the flash component at the second intensity, and receives the image from the imaging device.
Systems and methods are provided herein for minimizing retinal exposure to flash during image gathering for diagnosis. In an embodiment, a system captures a plurality of retinal images of different retinal regions. The system determines that a first portion of a first image does not meet a criterion while a second portion of the first image does meet the criterion, identifies a portion of the retina depicted in the first portion that does not meet the criterion, and determines whether the portion of the retina is depicted in a third portion of a second image and whether the third portion meets the criterion. Responsive to determining that the third portion meets the criterion, the system performs the diagnosis. Responsive to determining that the portion of the retina is not depicted in the second image, the system captures an additional image of the retinal region.
G16H 30/40 - ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
64.
IMAGE RETENTION AND STITCHING FOR MINIMAL-FLASH EYE DISEASE DIAGNOSIS
Systems and methods are provided herein for minimizing retinal exposure to flash during image gathering for diagnosis. In an embodiment, a system captures a plurality of retinal images of different retinal regions. The system determines that a first portion of a first image does not meet a criterion while a second portion of the first image does meet the criterion, identifies a portion of the retina depicted in the first portion that does not meet the criterion, and determines whether the portion of the retina is depicted in a third portion of a second image and whether the third portion meets the criterion. Responsive to determining that the third portion meets the criterion, the system performs the diagnosis. Responsive to determining that the portion of the retina is not depicted in the second image, the system captures an additional image of the retinal region.
Systems and methods are disclosed herein for adjusting flash intensity based on retinal pigmentation. In an embodiment, a processor determines a retinal pigmentation of a retina of an eye positioned at an imaging device. The processor commands the imaging device to adjust an intensity of a flash component from a first intensity to a second intensity based on the retinal pigmentation. The processor commands the imaging device to capture an image that is lit by the flash component at the second intensity, and receives the image from the imaging device.
A61B 3/14 - Arrangements specially adapted for eye photography
A61B 3/00 - Apparatus for testing the eyesInstruments for examining the eyes
A61B 3/10 - Objective types, i.e. instruments for examining the eyes independent of the patients perceptions or reactions
A61B 3/12 - Objective types, i.e. instruments for examining the eyes independent of the patients perceptions or reactions for looking at the eye fundus, e.g. ophthalmoscopes
66.
DYNAMIC ADJUSTMENT OF FLASH INTENSITY BASED ON RETINAL PIGMENTATION
Systems and methods are disclosed herein for adjusting flash intensity based on retinal pigmentation. In an embodiment, a processor determines a retinal pigmentation of a retina of an eye positioned at an imaging device. The processor commands the imaging device to adjust an intensity of a flash component from a first intensity to a second intensity based on the retinal pigmentation. The processor commands the imaging device to capture an image that is lit by the flash component at the second intensity, and receives the image from the imaging device.
A system for recording, storing and processing diagnostic information, including: a computer implementing a computer-readable media including digital data and ground truth; a registry constructed and arranged to store and associate transactions or accesses on the data; and a machine learning system that considers each learning step modification a microtransaction for the data used in that step and which is recorded in the transaction registry. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.
G16H 30/20 - ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G06N 3/084 - Backpropagation, e.g. using gradient descent
G16H 30/40 - ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
G06F 18/214 - Generating training patternsBootstrap methods, e.g. bagging or boosting
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
44 - Medical, veterinary, hygienic and cosmetic services; agriculture, horticulture and forestry services
Goods & Services
Downloadable medical software for diagnosis of disease conditions. Providing online non-downloadable medical software for diagnosis of disease conditions. Medical testing for diagnostic or treatment purposes; Providing medical diagnostic services of various disease conditions.
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
44 - Medical, veterinary, hygienic and cosmetic services; agriculture, horticulture and forestry services
Goods & Services
Downloadable medical software for diagnosis of disease conditions. Providing online non-downloadable medical software for diagnosis of disease conditions. Medical testing for diagnostic or treatment purposes; Providing medical diagnostic services of various disease conditions.
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
44 - Medical, veterinary, hygienic and cosmetic services; agriculture, horticulture and forestry services
Goods & Services
Downloadable medical software for diagnosis of disease conditions. Providing online non-downloadable medical software for diagnosis of disease conditions. Medical testing for diagnostic or treatment purposes; Providing medical diagnostic services of various disease conditions.
71.
DIAGNOSING SKIN CONDITIONS USING MACHINE-LEARNED MODELS
A diagnosis system trains a set of machine-learned diagnosis models that are configured to receive an image of a patient and generate predictions on whether the patient has one or more health conditions. In one embodiment, the set of machine-learned models are trained to generate predictions for images that contain two or more underlying health conditions of the patient. In one instance, the symptoms for the two or more health conditions are shown as two or more overlapping skin abnormalities on the patient. By using the architectures of the set of diagnosis models described herein, the diagnosis system can generate more accurate predictions for images that contain overlapping symptoms for two or more health conditions compared to existing systems.
A medical decision system generates, using a classifier, a medical decision based on an electronic patient record, which is compiled by medical decision system based on existing event data stored in cloud system. The medical decision system establishes a connection with a device at a second time and receives, from the device, additional event data. Each event in the additional event data has a timestamp corresponding to a first time at least a threshold amount of time prior to the second time, and a delta between the first time and the second time occurred at least in part due to the device being incapable of establishing the connection at the first time. The medical decision system determines whether the additional event data is valid. If the additional event data is valid, the medical decision system merges the additional event data into the existing event data and updates the medical decision.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
73.
MONITORING SURFACE CLEANING OF MEDICAL SURFACES USING VIDEO STREAMING
A cleaning wizard monitors and provides feedback for cleaning of medical equipment to ensure that cleaning is performed based on best practices. The cleaning wizard receives a video stream comprising an item of medical equipment and inputs a first set of video frames from the video stream into a first machine learning model. The first machine learning model is trained to output whether the first set of video frames corresponds to activity that initiates a cleaning protocol for the item of medical equipment. Responsive to the cleaning protocol being initiated, the cleaning wizard inputs a second set of video frames into a second machine learning model trained to output whether the second set of frames meets criteria of the cleaning protocol. Responsive to all criteria of the cleaning protocol being met, the cleaning wizard transmits a notification to an operator that the cleaning protocol is complete.
A47K 7/04 - Washing or cleaning devices, hand or mechanically operated
A61B 90/80 - Implements for cleaning or washing the skin of surgeons or patients
A61L 2/24 - Apparatus using programmed or automatic operation
B08B 7/04 - Cleaning by methods not provided for in a single other subclass or a single group in this subclass by a combination of operations
G08B 21/24 - Reminder alarms, e.g. anti-loss alarms
G16H 40/20 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
74.
USING A SET OF MACHINE LEARNING DIAGNOSTIC MODELS TO DETERMINE A DIAGNOSIS BASED ON A SKIN TONE OF A PATIENT
Systems and methods are disclosed herein for determining a diagnosis based on a base skin tone of a patient. In an embodiment, the system receives a base skin tone image of a patient, generates a calibrated base skin tone image by calibrating the base skin tone image using a reference calibration profile, and determines a base skin tone of the patient based on the calibrated base skin tone image. The system receives a concern image of a portion of the patient's skin, and selects a set of machine learning diagnostic models from a plurality of sets of candidate machine learning diagnostic models based on the base skin tone of the patient, each of the sets of candidate machine learning diagnostic models trained to receive the concern image and output a diagnosis of a condition of the patient.
G06V 10/40 - Extraction of image or video features
G06V 30/18 - Extraction of features or characteristics of the image
G16H 30/40 - ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
75.
Parallel optical coherence tomography apparatuses, systems, and related methods
Provided is a snapshot spectral domain optical coherence tomographer comprising a light source providing a plurality of beamlets; a beam splitter, splitting the plurality of beamlets into a reference arm and a sample arm; a first optical system that projects the sample arm onto multiple locations of a sample; a second optical system for collection of a plurality of reflected sample beamlets; a third optical system projecting the reference arm to a reflecting surface and receiving a plurality of reflected reference beamlets; a parallel interferometer that provides a plurality of interferograms from each of the plurality of sample beamlets with each of the plurality of reference beamlets; an optical image mapper configured to spatially separate the plurality of interferograms; a spectrometer configured to disperse each of the interferograms into its respective spectral components and project each interferogram in parallel; and a photodetector providing photon quantification.
A61B 3/10 - Objective types, i.e. instruments for examining the eyes independent of the patients perceptions or reactions
A61B 3/00 - Apparatus for testing the eyesInstruments for examining the eyes
A61B 3/12 - Objective types, i.e. instruments for examining the eyes independent of the patients perceptions or reactions for looking at the eye fundus, e.g. ophthalmoscopes
A61B 3/14 - Arrangements specially adapted for eye photography
An ocular alignment system for aligning a subject's eye with an optical axis of an ocular imaging device comprising one or more guide light and one or more baffle configured to mask the one or more guide light from view of the subject such that the one or more guide light is only visible to the subject when the eye of the subject is aligned with the optical axis of an ocular imaging system.
A61B 3/15 - Arrangements specially adapted for eye photography with means for aligning, spacing or blocking spurious reflection
A61B 3/00 - Apparatus for testing the eyesInstruments for examining the eyes
A61B 3/12 - Objective types, i.e. instruments for examining the eyes independent of the patients perceptions or reactions for looking at the eye fundus, e.g. ophthalmoscopes
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
Downloadable medical software for use in the diagnosis of disease conditions, namely, downloadable medical software for artificial intelligence processing and generation of reports for medical purposes Providing online non-downloadable medical software for use in the diagnosis of disease conditions, namely, online non-downloadable medical software for artificial intelligence processing and generation of reports for medical purposes
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
44 - Medical, veterinary, hygienic and cosmetic services; agriculture, horticulture and forestry services
Goods & Services
Downloadable medical software for diagnosis of disease conditions Providing online non-downloadable medical software for diagnosis of disease conditions Medical testing for diagnostic or treatment purposes; Providing medical diagnostic services of various disease conditions
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
44 - Medical, veterinary, hygienic and cosmetic services; agriculture, horticulture and forestry services
Goods & Services
Downloadable medical software for diagnosis of disease conditions Providing online non-downloadable medical software for diagnosis of disease conditions Medical testing for diagnostic or treatment purposes; Providing medical diagnostic services of various disease conditions
80.
Autonomous diagnosis of ear diseases from biomarker data
A fully autonomous system is used to diagnose an ear infection in a patient. For example, a processor receives patient data about a patient, the patient data comprising at least one of: patient history from medical records for the patient, one or more vitals measurements of the patient, and answers from the patient about the patient's condition. The processor receives a set of biomarker features extracted from measurement data taken from an ear of the patient. The processor synthesizes the patient data and the biomarker features into input data, and applies the synthesized input data to a trained diagnostic model, the diagnostic model comprising a machine learning model configured to output a probability-based diagnosis of an ear infection from the synthesized input data. The processor outputs the determined diagnosis from the diagnostic model. A service may then determine a therapy for the patient based on the determined diagnosis.
A61B 5/00 - Measuring for diagnostic purposes Identification of persons
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
A61B 1/227 - Instruments for performing medical examinations of the interior of cavities or tubes of the body by visual or photographical inspection, e.g. endoscopesIlluminating arrangements therefor for ears, i.e. otoscopes
81.
Data aggregation, integration and analysis system and related devices and methods
A system for recording, storing and processing diagnostic information, including: a computer implementing a computer-readable media including digital data and ground truth; a registry constructed and arranged to store and associate transactions or accesses on the data; and a machine learning system that considers each learning step modification a microtransaction for the data used in that step and which is recorded in the transaction registry. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.
G06K 9/62 - Methods or arrangements for recognition using electronic means
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 30/40 - ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
G06K 9/66 - Methods or arrangements for recognition using electronic means using simultaneous comparisons or correlations of the image signals with a plurality of references, e.g. resistor matrix references adjustable by an adaptive method, e.g. learning
82.
Autonomous diagnosis of a disorder in a patient from image analysis
A device receives an input image of a portion of a patient's body, and applies the input image to a feature extraction model, the feature extraction model comprising a trained machine learning model that is configured to generate an output that comprises, for each respective location of a plurality of locations in the input image, an indication that the input image contains an object of interest that is indicative of a presence of a disease state at the respective location. The device applies the output of the feature extraction model to a diagnostic model, the diagnostic model comprising a trained machine learning model that is configured to output a diagnosis of a disease condition in the patient based on the output of the feature extraction model. The device outputs the determined diagnosis of a disease condition in the patient obtained from the diagnostic model.
G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
G06F 18/214 - Generating training patternsBootstrap methods, e.g. bagging or boosting
Provided is a snapshot spectral domain optical coherence tomographer comprising a light source providing a plurality of beamlets; a beam splitter, splitting the plurality of beamlets into a reference arm and a sample arm; a first optical system that projects the sample arm onto multiple locations of a sample; a second optical system for collection of a plurality of reflected sample beamlets; a third optical system projecting the reference arm to a reflecting surface and receiving a plurality of reflected reference beamlets; a parallel interferometer that provides a plurality of interferograms from each of the plurality of sample beamlets with each of the plurality of reference beamlets; an optical image mapper configured to spatially separate the plurality of interferograms; a spectrometer configured to disperse each of the interferograms into its respective spectral components and project each interferogram in parallel; and a photodetector providing photon quantification.
A61B 3/00 - Apparatus for testing the eyesInstruments for examining the eyes
A61B 3/12 - Objective types, i.e. instruments for examining the eyes independent of the patients perceptions or reactions for looking at the eye fundus, e.g. ophthalmoscopes
84.
Systems and methods for alignment of the eye for ocular imaging
An ocular alignment system for aligning a subject's eye with an optical axis of an ocular imaging device comprising one or more guide light and one or more baffle configured to mask the one or more guide light from view of the subject such that the one or more guide light is only visible to the subject when the eye of the subject is aligned with the optical axis of an ocular imaging system.
A61B 3/15 - Arrangements specially adapted for eye photography with means for aligning, spacing or blocking spurious reflection
A61B 3/00 - Apparatus for testing the eyesInstruments for examining the eyes
A61B 3/12 - Objective types, i.e. instruments for examining the eyes independent of the patients perceptions or reactions for looking at the eye fundus, e.g. ophthalmoscopes
Disclosed is a system for qualifying medical images submitted by user for diagnostic analysis comprising: an image input module, configured to receive one or more image input by a user; an image protocol conformation module, configured to receive the one or more images from the image input module, and further configured to analyze each of the one or more images for conformity with a predefined protocol and wherein images that do not conform to the predefined protocol are flagged as non-conforming images; an image output module, configured to identify to the user each of the one or more images flagged as non-conforming and prompting the user to resubmit a new image for each of the non-conforming images; and an image resubmission module, configured to receive the user resubmitted image and provide the resubmitted image to the image protocol conformation module.
Provided is a method for training a neural network to detect features in a retinal image. The method may include the steps of: combining and randomizing feature images into a Training data set; combining and randomizing the feature images into a testing dataset; training a plurality of neural networks having different architectures using a subset of the training dataset while testing on a subset of the testing dataset; identifying the best neural network based on each of the plurality of neural networks performance on the testing data set; inputting images to the best neural network and identifying a limited number of false positives and false negative and adding the false positives and false negatives to the training dataset and testing dataset; and repeating the foregoing steps until an objective performance threshold is reached.
G06K 9/66 - Methods or arrangements for recognition using electronic means using simultaneous comparisons or correlations of the image signals with a plurality of references, e.g. resistor matrix references adjustable by an adaptive method, e.g. learning
G06K 9/46 - Extraction of features or characteristics of the image
G06K 9/62 - Methods or arrangements for recognition using electronic means
87.
Systems and methods for alignment of the eye for ocular imaging
An ocular alignment system for aligning a subject's eye with an optical axis of an ocular imaging device comprising one or more guide light and one or more baffle configured to mask the one or more guide light from view of the subject such that the one or more guide light is only visible to the subject when the eye of the subject is aligned with the optical axis of an ocular imaging system.
A61B 3/14 - Arrangements specially adapted for eye photography
A61B 3/15 - Arrangements specially adapted for eye photography with means for aligning, spacing or blocking spurious reflection
A61B 3/00 - Apparatus for testing the eyesInstruments for examining the eyes
A61B 3/12 - Objective types, i.e. instruments for examining the eyes independent of the patients perceptions or reactions for looking at the eye fundus, e.g. ophthalmoscopes
88.
Parallel optical coherence tomography apparatuses, systems, and related methods
Provided is a snapshot spectral domain optical coherence tomographer comprising a light source providing a plurality of beamlets; a beam splitter, splitting the plurality of beamlets into a reference arm and a sample arm; a first optical system that projects the sample arm onto multiple locations of a sample; a second optical system for collection of a plurality of reflected sample beamlets; a third optical system projecting the reference arm to a reflecting surface and receiving a plurality of reflected reference beamlets; a parallel interferometer that provides a plurality of interferograms from each of the plurality of sample beamlets with each of the plurality of reference beamlets; an optical image mapper configured to spatially separate the plurality of interferograms; a spectrometer configured to disperse each of the interferograms into its respective spectral components and project each interferogram in parallel; and a photodetector providing photon quantification.
A61B 3/00 - Apparatus for testing the eyesInstruments for examining the eyes
A61B 3/12 - Objective types, i.e. instruments for examining the eyes independent of the patients perceptions or reactions for looking at the eye fundus, e.g. ophthalmoscopes
Provided is a snapshot spectral domain optical coherence tomographer comprising: a light source providing a plurality of beamlets; a beam splitter, splitting the plurality of beamlets into a reference arm and a sample arm; a first optical system that projects the sample arm onto multiple locations of a sample; a second optical system for collection of a plurality of reflected sample beamlets; a third optical system projecting the reference arm to a reflecting surface and receiving a plurality of reflected reference beamlets; a parallel interferometer that provides a plurality of interferograms from each of the plurality of sample beamlets with each of the plurality of reference beamlets; an optical image mapper configured to spatially separate the plurality of interferograms; a spectrometer configured to disperse each of the interferograms into its respective spectral components and project each interferogram in parallel; and a photodetector providing photon quantification.
