Abstract: A method and system for remotely detecting a heart-related condition is presented. The method includes receiving a facial image of a patient, wherein the facial image is retrieved from a data store and captured from the patient; extracting at least one facial feature from the facial image of the patient, wherein the at least one facial feature is represented as numerical descriptors of facial attributes, wherein the numerical descriptors include surface features of the at least one facial feature; and classifying the extracted at least one facial feature using a classifier, wherein the classifier maps the extracted at least one facial feature to at least one score indicative of a stage of the heart-related condition.

Abstract: A method and system for remote diagnosis of a congestive heart failure in humans is presented. The method includes receiving a facial image of a patient, wherein the facial image is retrieved from a data store and captured from the patient; extracting, from the facial image, at least one facial feature indicative of a heart condition; classifying the extracted at least one facial feature using a classifier, wherein the classifier maps a plurality of candidate facial features to a plurality of scores indicating a stage of the heart condition; and determining a positive diagnosis and the stage of the heart condition of the patient based on the plurality of scores.

Abstract: A method and system for generating a classifier to classify facial images for cognitive disorder in humans. The system comprises receiving a labeled dataset including set of facial images, wherein each of the facial image is labeled depending on whether it represents a cognitive disorder condition; extracting, from each facial image in the set of facial images, at least one learning facial feature indicative of a cognitive disorder; and feeding the extracted facial features into a to produce a machine learning trained model to generate a classifier, wherein the classifier is generated and ready when the trained model includes enough facial features processed by a machine learning model.

Abstract: A method and system for remote diagnosis of a congestive heart failure in humans is presented. The method includes receiving a facial image of a patient, wherein the facial image is retrieved from a data store and captured from the patient; extracting, from the facial image, at least one facial feature indicative of a heart condition; classifying the extracted at least one facial feature using a classifier, wherein the classifier maps a plurality of candidate facial features to a plurality of scores indicating a stage of the heart condition; and determining a positive diagnosis and the stage of the heart condition of the patient based on the plurality of scores.

Abstract: A method and system for generating a classifier to classify facial images for cognitive disorder in humans. The system comprises receiving a labeled dataset including set of facial images, wherein each of the facial image is labeled depending on whether it represents a cognitive disorder condition; extracting, from each facial image in the set of facial images, at least one learning facial feature indicative of a cognitive disorder; and feeding the extracted facial features into a to produce a machine learning trained model to generate a classifier, wherein the classifier is generated and ready when the trained model includes enough facial features processed by a machine learning model.

Abstract: A method and system for remote diagnosis of a cognitive disorder in humans are provided. The system comprises receive, over a network, at least one facial image of a patient; extracting, from the at least one facial image, at least one facial feature indicative of a cognitive decline; classifying the at least one extracted facial feature using a classifier, wherein the classifier maps a plurality of candidate facial features to a score indicating a stage of a cognitive decline; and determining a positive diagnosis of a cognitive decline of the patient, based on the score provided by the classifier in response to the at least one extracted facial feature.

Abstract: A method and system for remote diagnosis of a cognitive disorder in humans are provided. The system comprises receive, over a network, at least one facial image of a patient; extracting, from the at least one facial image, at least one facial feature indicative of a cognitive decline; classifying the at least one extracted facial feature using a classifier, wherein the classifier maps a plurality of candidate facial features to a score indicating a stage of a cognitive decline; and determining a positive diagnosis of a cognitive decline of the patient, based on the score provided by the classifier in response to the at least one extracted facial feature.