Machine-Learned Diagnosis Assistance for ADNC Risk Prediction
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Solution Overview
Problem
Current methods struggle to accurately predict which patients with Alzheimer's disease neuropathologic change (ADNC) will develop Alzheimer's disease within a prescribed period, making it difficult to determine when to administer disease-modifying therapies.
Innovation Solution
A diagnosis assistance device using a machine-learned prediction algorithm that segments brain images into gray matter, white matter, and lateral ventricle, calculates t- and p-values, and z-values to predict the likelihood of Alzheimer's disease development, employing support vector machines for learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all patients with ADNC are targeted for disease-modifying therapy, then treatment coverage is maximized, but treatment cost and resource allocation efficiency deteriorate
Solution Approach 1:
The patent segments ADNC patients into high-risk and low-risk groups using machine learning classification. The system divides the patient population based on predicted progression probability to Alzheimer's disease, enabling differentiated treatment strategies. This segmentation resolves the contradiction by identifying which patients truly need intervention, improving prediction accuracy for treatment eligibility while avoiding unnecessary treatment for low-risk patients.
Solution Approach 2:
The patent changes the parameter of prediction probability threshold to classify patients into different risk groups. By adjusting the probability threshold (e.g., >30% vs. <30%), the system can optimize the balance between sensitivity and specificity in identifying patients who need treatment. This parameter change enables the system to achieve high prediction accuracy for treatment eligibility while managing resource allocation efficiently.
2Measurement precision
If conventional diagnosis methods are used, then diagnostic simplicity is maintained, but prediction precision for individual patients deteriorates
Solution Approach 1:
The patent replaces conventional mechanical diagnostic methods with machine learning-based prediction algorithms. Instead of relying solely on clinical judgment and simple diagnostic criteria, the system uses computational models trained on extensive patient data to predict individual progression risk. This substitution achieves high prediction precision for individual patients while the automated nature of the system manages complexity through algorithmic processing rather than manual analysis.
Solution Approach 2:
The patent creates a virtual copy of the patient's brain structure and clinical data in the machine learning model. The system makes a simplified representation (copy) of the complex patient information and processes this copy through the prediction algorithm. This copying approach enables high prediction precision by analyzing multiple features simultaneously while keeping the actual system manageable through data abstraction and model simplification.
3Reliability
If early diagnosis is pursued, then treatment timing is optimized, but diagnostic uncertainty increases
Solution Approach 1:
The patent implements a feedback mechanism where the machine learning model continuously refines its predictions based on new patient data and outcomes. The system uses feedback from actual disease progression to improve the prediction algorithm's accuracy over time. This feedback loop enables early diagnosis with increasing reliability, as the model learns from real-world outcomes and adjusts its prediction precision accordingly.
Solution Approach 2:
The patent performs preliminary prediction of disease progression risk before final diagnostic confirmation. By using machine learning to predict which patients are most likely to develop Alzheimer's disease, the system can prioritize monitoring and intervention for high-risk patients. This preliminary action approach enables early identification of at-risk patients while the continuous learning process improves diagnostic precision over time.
Data Source
AI summary
The possibility that an ADNC patient will develop Alzheimer's disease is predicted with high accuracy. A diagnosis assistance device 2 predicts a possibility that a subject who has ADNC will develop Alzheimer's disease within a prescribed period, the diagnosis assistance device 2 comprising a prediction unit 23 that predicts the possibility that the subject will develop Alzheimer's disease within a prescribed period, according to a machine-learned prediction algorithm D4.