Facial Image Classifier for Cognitive Disorder Detection
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Solution Overview
Problem
Current methods for detecting dementia, such as the Mini Mental State Examination (MMSE), are time-consuming, prone to inaccurate results, and not suited for early detection, lacking dementia-specific hallmarks, which hinders timely diagnosis and increases the burden on patients and healthcare systems.
Innovation Solution
A machine learning-based system that classifies facial images for cognitive disorders by extracting learning facial features from labeled datasets and using a trained model to generate a classifier, enabling early detection and potentially reducing the need for in-person doctor visits through remote diagnosis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current dementia detection methods like MMSE are used, then diagnosis can be obtained, but diagnostic time is excessive and accuracy is compromised
Solution Approach 1:
The patent replaces the mechanical manual assessment system (physician administering MMSE tests) with an automated machine learning system that processes facial images. The ML classifier automatically extracts facial features and generates dementia risk scores, eliminating the need for time-consuming manual cognitive tests while maintaining or improving diagnostic accuracy through objective biomarker analysis.
Solution Approach 2:
The patent creates a digital copy of the patient's facial appearance through captured images, which are then analyzed by the ML system. This facial image copy serves as a surrogate for direct cognitive testing, allowing the system to infer cognitive status from facial biomarkers without requiring the patient to undergo lengthy mental state examinations.
2Reliability
If traditional cognitive assessments are applied, then dementia can be detected, but early detection capability is limited
Solution Approach 1:
The patent performs preliminary detection of dementia risk by analyzing facial biomarkers before traditional cognitive symptoms manifest. The ML classifier can identify subtle facial feature changes that precede clinical dementia diagnosis, enabling early intervention. The system processes facial images to generate risk scores that indicate cognitive status before patients fail standard cognitive tests.
3Adaptability or versatility
If comprehensive cognitive tests are administered, then diagnostic coverage is improved, but complexity of the diagnostic process increases
Solution Approach 1:
The patent extracts only the essential diagnostic information needed for dementia detection by analyzing specific facial features through the ML system. Instead of administering comprehensive cognitive tests covering multiple domains, the system extracts relevant biomarkers from facial images alone, simplifying the diagnostic process while maintaining diagnostic coverage for dementia and cognitive impairment.
Data Source
AI summary
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.