Human Head Image Analysis for Early Dementia Progression Estimation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current diagnostic methods for dementia, particularly Alzheimer's disease, are invasive, costly, time-consuming, and lack accuracy in early detection, leading to underdiagnosis and hindering research and treatment development.
Innovation Solution
A non-invasive method using machine learning analysis of human head images to detect patterns related to dementia symptoms, employing computer devices and servers to process images and estimate dementia progression through machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional diagnostic methods (MRI, PET, lumbar puncture) are used, then diagnostic accuracy is improved, but cost and invasiveness increase
Solution Approach 1:
The patent uses 2D photographs as external copies or proxies to represent internal brain structures. Instead of directly imaging the brain with expensive and invasive methods, the system captures external head images that contain indirect information about brain atrophy, creating a non-invasive diagnostic approach that replicates diagnostic value through alternative means
Solution Approach 2:
The patent replaces complex mechanical and physical diagnostic systems (MRI machines, PET scanners, lumbar puncture procedures) with a computational image analysis system. Machine learning algorithms process 2D photographs to detect patterns of brain atrophy, substituting sophisticated medical imaging hardware with software-based analysis of external images
2Measurement precision
If traditional diagnostic methods are used, then diagnostic accuracy is improved, but time consumption increases
Solution Approach 1:
The patent performs preliminary data collection by capturing 2D photographs that can be immediately analyzed without requiring complex setup or preparation. The images serve as pre-processed data that can be rapidly evaluated by machine learning algorithms, eliminating the time-consuming scheduling and execution of MRI or PET scans
Solution Approach 2:
The patent replaces time-consuming mechanical imaging processes with rapid computational analysis. Machine learning models can process multiple 2D images and generate diagnostic assessments in minutes, substituting the hours-long procedures of traditional neuroimaging with instant software-based evaluation
3Measurement precision
If traditional diagnostic methods are used, then diagnostic capability is improved, but accessibility and ease of use worsen
Solution Approach 1:
The patent uses widely available 2D photographs as diagnostic input, replacing the need for specialized medical imaging equipment. Since everyone has access to cameras and photographs, this approach democratizes diagnostic capability, allowing assessment in primary care settings, community clinics, and even at home without requiring expensive MRI or PET facilities
Solution Approach 2:
The patent enables diagnostic assessment through simple photograph capture and automated machine learning analysis, reducing the need for specialized medical personnel. The system performs self-service diagnostic evaluation by automatically processing images and generating assessments, making the diagnostic process as accessible as taking a photograph
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.