Brain Disease Risk Modeling Using Cerebrovascular Shape and Flow
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Solution Overview
Problem
Conventional methods for predicting brain disease risk rely solely on patient symptoms and statistical data, failing to comprehensively consider cerebrovascular shape information and cerebral blood flow information.
Innovation Solution
A method involving a brain disease risk analysis model trained on datasets including cerebrovascular shape, blood flow, and patient information, allowing personalized risk prediction based on these factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods use only patient symptoms and statistical data for brain disease prediction, then the prediction process is simple, but the prediction accuracy and comprehensiveness are insufficient
Solution Approach 1:
The patent merges multiple data sources including patient symptoms, statistical data, cerebrovascular shape information from MRA images, and cerebral blood flow information into a unified prediction model. This combination of diverse data types enables comprehensive brain disease risk assessment while maintaining system feasibility through integrated processing
2Adaptability or versatility
If cerebrovascular shape information and blood flow information are incorporated into brain disease prediction, then the comprehensiveness of diagnosis improves, but the complexity of data processing and analysis increases
Solution Approach 1:
The patent segments the complex diagnostic process into distinct modules: one for extracting cerebrovascular shape information from MRA images, another for analyzing cerebral blood flow information, and a third for integrating these with patient symptoms and statistical data. This modular segmentation enables comprehensive diagnosis while managing processing complexity through structured organization
3Reliability
If a comprehensive analysis model considering multiple factors is developed, then the personalized risk prediction capability improves, but the computational resources and model training complexity increase
Solution Approach 1:
The patent performs preliminary processing and feature extraction on MRA images and blood flow data before feeding them into the prediction model. By pre-processing the data to extract relevant features such as cerebrovascular shape characteristics and blood flow patterns, the system reduces the computational burden during model training and inference, enabling reliable personalized predictions without excessive complexity
Data Source
AI summary
A method for predicting the risk of brain disease according to an embodiment of the present disclosure includes the steps of: acquiring a risk analysis model for brain disease, which has been trained; acquiring target shape information of the brain vessels of the subject patient; acquiring target blood flow information of the brain vessels of the subject patient; acquiring target patient information of the subject patient; and inputting the acquired target shape information, target blood flow information, and target patient information into the risk analysis model for brain disease and acquiring the risk value of brain disease output through the risk analysis model for brain disease.