Deep Learning VAD Risk Prediction System
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Solution Overview
Problem
Current studies on vertebral artery dissection (VAD) risk factors are conducted independently and lack a comprehensive system to predict VAD probability using combined medical image and clinical report data.
Innovation Solution
A method and apparatus utilizing deep learning classification models to determine VAD risk probability by extracting biomarkers from medical images and patient history information from clinical reports, incorporating centerline extraction and lumen segmentation algorithms to process multi-modality data such as MRI and MRA images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple independent studies on VAD risk factors are conducted separately, then each study can focus on specific risk factors, but comprehensive prediction capability is lost
Solution Approach 1:
The patent combines multiple independent risk factor studies into a unified deep learning classification model that processes medical image information, clinical report information, and patient history information together. This merging allows the system to maintain the specificity of individual risk factor analysis while achieving comprehensive VAD prediction capability through integrated multi-modal data processing.
2Measurement precision
If a comprehensive system gathering multiple risk factors is built, then prediction accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments the comprehensive prediction system into distinct functional modules: a medical image processing module that extracts biomarkers from imaging data, a clinical report processing module that extracts patient history information, and a deep learning classification model that integrates both data types. This segmentation manages system complexity by organizing multi-modal data processing into separate, specialized components while maintaining overall integration for accurate VAD probability prediction.
3Measurement precision
If deep learning models process multi-modality data, then diagnostic precision improves, but computational requirements increase
Solution Approach 1:
The patent extracts key biomarkers from medical image information and essential patient history information from clinical reports before feeding them into the deep learning classification model. This extraction process filters out redundant data, retaining only the most relevant features for VAD prediction, thereby reducing computational energy consumption while preserving diagnostic precision.
Data Source
AI summary
A method of determining a risk probability of vertebral artery dissection (VAD) in a patient, including receiving medical image information of the patient and clinical report information of the patient; extracting at least one biomarker corresponding to a vertebral artery segment included in the medical image information; extracting patient history information from the clinical report information; and determining the risk probability of VAD using a deep learning classification model based on the extracted at least one biomarker and the extracted patient history information.