Head Image Analysis for Early Dementia Progress Estimation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current diagnostic methods for dementia, such as PET scans and CSF biomarkers, are invasive, costly, and lack accuracy in early detection, while non-invasive methods like facial expression analysis are unreliable and often detect symptoms too late in the disease progression.
Innovation Solution
A non-invasive method using machine learning to analyze images of a subject's head for patterns related to dementia symptoms, employing machine learning models to estimate dementia progression through external physical manifestations detectable by image analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If PET scans and CSF biomarkers are used for dementia diagnosis, then measurement precision is improved, but device complexity and cost increase significantly
Solution Approach 1:
The patent uses 2D images of the head as a simplified copy or representation of the actual brain structure. Instead of directly analyzing complex brain tissue through invasive procedures, the system captures external head images that contain correlated information about internal brain conditions, thereby reducing diagnostic complexity while maintaining detection capability
Solution Approach 2:
The patent replaces complex mechanical and chemical diagnostic systems (PET scanners, CSF analysis equipment) with a computational image analysis system. The mechanical imaging equipment is substituted with standard cameras or image capture devices, and the chemical analysis of CSF biomarkers is replaced with machine learning-based pattern recognition in images
2Measurement precision
If invasive diagnostic methods like lumbar puncture are used, then measurement precision is improved, but object-affected harmful factors increase
Solution Approach 1:
The system uses external head images as a non-invasive copy that correlates with internal brain biomarker information. This eliminates the need for invasive procedures like lumbar puncture while still providing diagnostic information about dementia progression through pattern recognition in the captured images
Solution Approach 2:
The patent introduces an intermediary medium (the external head image) that mediates between the diagnostic goal and the patient's body. Instead of directly accessing internal biomarkers through invasive means, the system uses the head's external appearance as an intermediary that contains correlated information about internal brain conditions
3Ease of operation
If facial expression analysis is used for non-invasive dementia detection, then ease of operation is improved, but reliability deteriorates due to late detection
Solution Approach 1:
The patent segments the head image into multiple anatomical regions (face, scalp, hair, ears, eyes, nose, mouth, teeth, tongue, neck, shoulders, collarbone, hands, fingers, nails) and analyzes specific features within each segment. This detailed segmentation allows detection of subtle structural changes that occur earlier in disease progression, improving reliability while maintaining ease of operation through automated analysis
Solution Approach 2:
The system applies different analysis methods and feature extraction techniques to different local regions of the head based on their specific characteristics. Each anatomical segment is analyzed for locally relevant features, allowing the system to detect early disease manifestations in specific regions while maintaining overall diagnostic reliability
4Measurement precision
If current diagnostic methods are used, then measurement precision is improved, but loss of time increases due to complex procedures
Solution Approach 1:
The system performs preliminary automated processing of head images, including detection of anatomical landmarks, segmentation of regions, and extraction of relevant features before final analysis. This preliminary action prepares the data in advance, allowing faster and more efficient diagnostic evaluation while maintaining measurement precision through systematic feature analysis
Data Source
AI summary
A system for non-invasive estimation of dementia progression. The system includes a computer device and a server. The computer device obtains an image of a subject's head from at least one angle. The server and/or computer device includes a plurality of machine learning models configured to: analyze the image for patterns related to dementia symptoms; and estimate progress of said dementia symptoms of said subject based on the analysis. The server and/or computer device pre-processes the image by performing a plurality of pre-processing steps comprising: importing the image; detecting eyes and shape of the head based on a previously trained machine learning model; rotating the image based on detection of the eyes and shape of the head; normalizing the image to one standard.