Facial Image Analysis for Accurate Heart Failure Estimation
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Solution Overview
Problem
Existing systems lack the ability to accurately estimate the degree of heart failure in patients using still or video images of their facial region.
Innovation Solution
A heart failure estimation system that includes a camera, an acquisition unit to capture facial images or videos, an extraction unit to identify feature parameters, and an estimation unit to calculate the degree of heart failure based on these parameters using statistical or machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If facial image analysis is used to estimate heart failure degree, then diagnostic efficiency is improved and workload is reduced, but measurement precision of heart failure degree may be insufficient
Solution Approach 1:
The system segments the heart failure estimation process into multiple independent feature extraction components: facial edema detection, eye closure state analysis, mouth opening state analysis, and skin color evaluation. Each segment extracts specific parameters that are then integrated to form a comprehensive heart failure degree estimation, improving both efficiency and precision.
Solution Approach 2:
The system transitions from traditional single-dimensional diagnostic methods to multi-dimensional analysis by evaluating multiple facial features simultaneously (edema, eye closure, mouth opening, skin color). This dimensional expansion allows the system to capture complex heart failure manifestations through diverse facial parameters, enhancing measurement precision while maintaining diagnostic efficiency.
2Measurement precision
If multiple feature parameters are extracted from facial images, then estimation accuracy is improved, but device complexity increases
Solution Approach 1:
The system employs a universal facial image analysis framework that can extract multiple feature parameters (edema, eye closure, mouth opening, skin color) from the same input image. This multi-functional approach allows a single imaging system to perform comprehensive heart failure assessment without requiring additional specialized devices, thus improving precision while controlling complexity.
Solution Approach 2:
The system uses the patient's own facial features as the diagnostic target, eliminating the need for external sensors or additional equipment. By leveraging naturally present facial characteristics that change with heart failure progression, the system achieves high estimation accuracy using only standard imaging equipment, thereby avoiding increased device complexity.
Data Source
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AI summary
A heart failure estimation device acquires an image or video obtained by imaging the face of a patient. The heart failure estimation device extracts at least one feature parameter from the image or video. The heart failure estimation device estimates a degree of heart failure of the patient on the basis of the at least one feature parameter.