A method and apparatus for predicting early alzheimer's disease based on lymphoid system features
By combining DTI-ALPS technology and visual semantic segmentation model, a 3D-UNet and MLP model is constructed for early screening of Alzheimer's disease, which solves the problems of long analysis time and inaccurate prediction in existing technologies, and achieves efficient and accurate early Alzheimer's disease prediction.
CN121506493BActive Publication Date: 2026-07-24SECOND MEDICAL CENT OF CHINESE PLA GENERAL HOSPITAL
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Patent Information
- Application Number
- CN202511707124.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-20
- Publication Date
- 2026-07-24
- Estimated Expiration
- 2045-11-20
AI Technical Summary
Technical Problem
Existing technologies for early screening of Alzheimer's disease suffer from problems such as long PVS-VF analysis time, excessive manual intervention, and insufficient predictive flexibility and generalization.
Method used
We used DTI-ALPS technology and a visual semantic segmentation model to analyze the ALPS index and PVS-VF scores of multiple brain regions, combined with an MLP model for binary classification prediction, and constructed a 3D-UNet and MLP model for semantic segmentation and early Alzheimer's disease prediction.
Benefits of technology
Reduce human intervention, improve processing efficiency, increase the abundance of PVS-VF features in multiple brain regions, and enhance prediction accuracy, flexibility, and generalization.
✦ Generated by Eureka AI based on patent content.
Abstract
The embodiment of the present application relates to a kind of method and device based on early Alzheimer's disease prediction of lymphoid system characteristics, the method comprises: gathering original sample set, based on 3D-UNet model, first segmentation model is constructed, based on MLP model, first prediction model is constructed;Based on original sample set, first, second data set is constructed;First segmentation model is trained based on first data set;First prediction model is trained based on second data set;After model training ends, the brain DTI image and brain 3D-T2 weighted image of subject are received, and according to brain DTI image, left and right brain ALPS index analysis is carried out based on DTI-ALPS technique, and based on first segmentation model, semantic segmentation is carried out to brain 3D-T2 weighted image, and according to segmentation result, four brain area perivascular space volume fraction analysis is carried out, and using first prediction model, according to two kinds of analysis results, prediction is carried out.The present application can improve processing efficiency, improve prediction accuracy.
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