Patient-Specific Stroke Risk Prediction Using LA and LAA Analysis
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Solution Overview
Problem
Current clinical practices for predicting cardio-embolic stroke risk are limited by their non-specificity and reliance on statistical indicators with large variances, failing to accurately assess individual patient risks due to time plasticity and weak patient specificity.
Innovation Solution
A method and system for patient-specific ischemic stroke risk prediction using automated analysis of the left atrium (LA) and left atrial appendage (LAA) from medical images, employing computational modeling and machine learning to extract and analyze morphological, hemodynamic, and electrophysiological features, including new factors like LAA morphology complexity and relative residence time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional clinical methods (history, physical examination, neuroimaging, ECG, laboratory and echocardiographic data) are used for stroke risk prediction, then comprehensive patient evaluation is achieved, but the risk indices have large variances, time plasticity (uncertainty), and weak patient specificity
Solution Approach 1:
The patent segments the left atrium into two distinct parts: the main body (LA) and the appendage (LAA). This segmentation allows for separate morphological analysis of each structure, extracting specific features like LA volume, LAA volume, and LAA complexity metrics. By analyzing these segmented components individually rather than as a single mass, the system achieves more precise and patient-specific risk prediction, overcoming the weak specificity of traditional comprehensive but non-differentiated clinical methods.
2Ease of operation
If traditional statistical indicators are used for risk assessment, then clinical evaluation is simplified, but the indices exhibit large variances and time plasticity leading to reduced prediction reliability
Solution Approach 1:
The patent replaces traditional mechanical/statistical risk assessment methods with an automated computational system. Machine learning algorithms automatically process medical images to extract morphological features of the LA and LAA, compute hemodynamic metrics, and generate risk predictions. This substitution eliminates manual calculation errors and time plasticity associated with traditional statistical indicators, providing consistent, precise, and automated risk assessment that maintains ease of operation while dramatically improving measurement precision.
3Measurement precision
If detailed anatomical analysis of LA and LAA is performed using computational modeling and machine learning, then patient-specific risk prediction precision is improved, but system complexity increases
Solution Approach 1:
The patent introduces an automated machine learning system as an intermediary between medical image acquisition and clinical decision-making. This intermediary automatically performs complex tasks including image segmentation, feature extraction from LA and LAA structures, hemodynamic metric computation, and risk prediction. By placing this intelligent intermediary in the workflow, the system manages the complexity of detailed anatomical analysis while maintaining ease of use for clinicians, as the automated system handles all complex processing without requiring manual intervention.
Data Source
AI summary
A system and method for medical image based patient-specific ischemic stroke risk prediction is disclosed. Left atrium (LA) and left atrium appendage (LAA) measurements are extracted from medical image data of a patient. Derived metrics for the LA and LAA of the patient are computed using a patient-specific computational model of cardiac function based on the LA and LAA measurements extracted from the medical image data of the patient. A stroke risk score for the patient is calculated based on the extracted LA and LAA measurements and the computed derived metrics for the LA and LAA of the patient using a trained machine learning based classifier, which inputs the extracted LA and LAA measurements and the computed derived metrics for the LA and LAA as features.