Remote Heart Failure Diagnosis via Facial Feature Analysis
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Solution Overview
Problem
Current diagnostic methods for heart failure are limited in their ability to detect deterioration in early stages and require in-person visits, making it challenging to monitor and track conditions effectively for patients.
Innovation Solution
A remote diagnosis system that uses facial image analysis to extract features indicative of heart conditions, classify them using a trained classifier, and determine the stage of heart failure based on scores, enabling remote monitoring and early detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current diagnostic methods are used, then diagnosis can be performed at medical facilities, but early detection capability is limited and in-person visits are required
Solution Approach 1:
The patent uses facial images (copies/representations of the patient's physical state) to diagnose heart failure conditions. Instead of requiring the patient's physical presence at a medical facility, the system analyzes facial image data that can be captured remotely, thereby improving accessibility while maintaining diagnostic capability
Solution Approach 2:
The patent replaces the mechanical requirement of in-person physical examination with an automated image analysis system. The classifier processes facial images to detect signs of heart failure, substituting the need for direct physical interaction between patient and physician with an automated computational system
2Productivity
If remote diagnosis using facial image analysis is implemented, then early detection and remote monitoring are enabled, but system complexity increases
Solution Approach 1:
The patent employs a multi-functional classifier system that can identify multiple conditions (heart failure, kidney failure, liver failure, diabetes, anemia, cognitive impairment) using the same facial image analysis framework. This universal approach enables remote monitoring of various health conditions through a single system, improving productivity despite the inherent complexity
3Reliability
If in-person doctor visits are required, then accurate diagnosis can be performed, but patient tracking and monitoring become challenging
Solution Approach 1:
The patent implements a feedback mechanism where the classifier processes facial images and generates diagnostic results that can be used to track patient conditions over time. The system can compare current facial images with previous images to monitor disease progression or response to treatment, enabling continuous patient tracking while maintaining diagnostic reliability through the established classification algorithm
Data Source
AI summary
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.