Medical Data Processing for Neurodegenerative Diagnosis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for diagnosing neurodegenerative disorders like Alzheimer's and Parkinson's are challenging due to difficulty in early detection and inconsistent, subjective assessments, lacking standardization and incorporating non-image data for holistic diagnosis.
Innovation Solution
A system and method that access and process both image and non-image deviation scores from patient data compared to standardized reference data, generating visual outputs to facilitate consistent and standardized diagnosis, incorporating multiple imaging modalities and longitudinal data analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If highly-trained medical image experts perform diagnosis based on deviation images, then diagnostic expertise and accuracy are improved, but diagnostic consistency and standardization deteriorate due to inherent subjectivity
Solution Approach 1:
The patent introduces an automated computer-based analysis system as an intermediary between the deviation images and the final diagnosis. This system applies standardized algorithms to quantify deviations and generate objective measurements, serving as a mediator that reduces human subjectivity while preserving expert oversight. The automated system processes images through consistent computational methods, ensuring reliability, while experts review the standardized outputs to maintain diagnostic accuracy.
2Illumination intensity
If only image data is used for diagnosis, then imaging visualization is improved, but holistic patient assessment deteriorates due to lack of non-image data integration
Solution Approach 1:
The patent merges multiple data types including image data, non-image clinical data, laboratory results, and patient history into a unified diagnostic framework. The system integrates these diverse data sources through standardized processing pipelines, combining visual deviations with quantitative clinical measurements to create a comprehensive patient assessment that preserves both imaging quality and holistic information.
Solution Approach 2:
The diagnostic system is designed with multi-functionality to handle various data types uniformly. It processes both image and non-image data through the same standardized analysis framework, enabling the system to perform multiple diagnostic functions simultaneously while maintaining consistency across different data modalities and providing a unified holistic view.
3Adaptability or versatility
If subjective diagnostic calls are made by experts, then clinical judgment flexibility is improved, but standardization and reproducibility deteriorate
Solution Approach 1:
The system implements dynamic adaptability through adjustable parameters and configurable analysis protocols that can be tailored to different clinical scenarios while maintaining standardized core processing. The automated analysis engine can adapt to various disease types and patient populations through parameter adjustments, yet preserves reproducible results through consistent algorithmic execution and standardized measurement protocols.
Data Source
AI summary
A data processing technique is provided. In one embodiment, a computer-implemented method includes accessing patient image and non-image deviation scores derived through respective comparisons of patient image and non-image data to standardized image and non-image data. The method may also include processing the image and non-image deviation scores to generate a visual output indicative of differences between the patient image and non-image data, and the standardized image and non-image data, respectively. Further, the method may include displaying the visual output. Additional methods, systems, and manufactures are also disclosed.