AI Prediction Device for Beta-Amyloid Conversion Status
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Solution Overview
Problem
Current treatments for Alzheimer’s disease primarily focus on symptom relief after beta-amyloid deposition, with limited options for preventing the disease before deposition occurs, necessitating a method for early prediction of Alzheimer’s development in β-amyloid negative patients.
Innovation Solution
A beta-amyloid positive conversion target prediction device utilizing a pretrained AI model that integrates patient information such as age, gender, apolipoprotein genotype, and standardized uptake value ratio (SUVR) from amyloid PET tests to predict the likelihood of β-amyloid positive conversion in β-amyloid negative patients.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current treatments focus on symptom relief after beta-amyloid deposition, then symptom management is improved, but early disease prevention capability deteriorates
Solution Approach 1:
The patent applies preliminary action by developing an AI prediction model that identifies patients at risk of beta-amyloid positive conversion before the actual deposition occurs. The system uses multiple input features including age, gender, APOE genotype, and SUVR to predict future conversion status, enabling primary prevention interventions to be initiated earlier in the disease trajectory.
2Measurement precision
If comprehensive patient data including genetic information and PET results is integrated, then prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The patent merges multiple data sources and analysis components into a unified AI prediction system. It combines demographic information, genetic data (APOE genotype), and imaging metrics (SUVR from amyloid PET) into a single integrated model that processes all inputs simultaneously to generate conversion probability predictions, thereby improving accuracy while managing complexity through consolidation.
Data Source
AI summary
A beta (p)-amyloid positive conversion target prediction device is provided. The β-amyloid positive conversion target prediction device includes: a patient information analyzer configured to provide first input information by differentiating age and gender of a β-amyloid negative patient based on basic information of a patient, a genotype analyzer configured to provide second input information for determining an apolipoprotein genotype status of the patient, a standardized update value ratio (SUVR) analyzer configured to provide an SUVR calculated from amyloid positron emission tomography (PET)test results of the patient as third input information and an artificial intelligence (AI) model configured to provide prediction results related to a β-amyloid positive conversion status of the β-amyloid negative patient based on at least one of the first input information, the second input information, and the third input information.