Machine Learning Cognitive Disease Screening Without Molecular Imaging
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Solution Overview
Problem
Current methods for diagnosing cognitive diseases like Alzheimer's and Parkinson's are inefficient and resource-intensive, relying heavily on expensive molecular imaging techniques, and lack effective tools for identifying subjects likely to progress to the disease or respond to treatments, leading to high attrition rates and lengthy clinical trials.
Innovation Solution
A medical system utilizing machine learning models trained on clinical and imaging data to predict disease pathology and progression without requiring molecular imaging, enabling pre-screening and stratification of subjects for clinical trials.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If molecular imaging procedures (PET scans) are used to diagnose cognitive diseases and identify subjects for clinical trials, then diagnostic accuracy and subject selection precision are improved, but cost and resource requirements increase significantly
Solution Approach 1:
The patent creates a digital copy or surrogate of the molecular imaging diagnostic process through machine learning models. These models are trained on imaging data to reproduce the diagnostic capabilities of PET scans, enabling accurate subject identification without requiring actual molecular imaging procedures for every patient screening.
Solution Approach 2:
The patent replaces expensive, resource-intensive molecular imaging with cheaper, computationally-based diagnostic tools. The machine learning models serve as disposable or reusable software solutions that provide diagnostic accuracy at a fraction of the cost and resource requirement of actual PET scanning procedures.
2Measurement precision
If molecular imaging procedures are used for subject screening in clinical trials, then subject selection quality is improved, but trial duration and resource consumption increase
Solution Approach 1:
The patent performs preliminary diagnostic assessment using machine learning models before committing subjects to lengthy molecular imaging procedures. By pre-screening subjects with faster computational tools, the system identifies likely candidates who then undergo molecular imaging only if needed, reducing overall trial duration while maintaining selection quality.
Solution Approach 2:
The machine learning models create a temporal copy of the diagnostic process that executes much faster than actual molecular imaging. This digital surrogate enables rapid subject evaluation, allowing trials to proceed more quickly while maintaining the same diagnostic rigor through the trained models.
3Measurement precision
If extensive diagnostic testing is performed on all potential subjects, then diagnostic completeness is improved, but resource efficiency deteriorates due to redundant testing
Solution Approach 1:
The patent divides the diagnostic process into segments or stages. The machine learning model handles the initial screening and identification of subjects with high probability of disease presence, reserving molecular imaging procedures only for those who cross a certain threshold. This segmentation eliminates redundant testing in subjects unlikely to have the disease while maintaining diagnostic completeness for at-risk individuals.
Solution Approach 2:
The patent applies partial diagnostic action by using machine learning models to assess subjects without performing complete molecular imaging procedures on everyone. The system performs just enough diagnostic work (through the model) to identify high-probability cases, avoiding excessive testing on low-probability subjects while maintaining overall diagnostic completeness for the target population.
Data Source
AI summary
A medical system useful in the determination of future disease progression in a subject. More specifically the present invention applies machine learning techniques to aid prediction of disease pathology and clinical outcomes in subjects presenting with symptoms of cognitive decline and to expedite clinical development of novel therapeutics.