Plaque Vulnerability Assessment via Multi-Modal Classifier
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Solution Overview
Problem
Current methods for assessing plaque vulnerability in medical imaging are subjective and rely on limited imaging information, leading to variability in predicting plaque rupture, which can be life-threatening in critical vessels like coronaries and cerebral arteries.
Innovation Solution
A machine-implemented classifier combines anatomical, morphological, hemodynamic, and biochemical features from medical imaging, hemodynamic sensors, and blood tests to calculate a risk score for plaque rupture, providing a more objective assessment by integrating multiple data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple medical imaging modalities are used to analyze plaque, then the ability to accurately predict plaque rupture improves, but the subjectivity and variability depending on the person predicting remains
Solution Approach 1:
The patent replaces the mechanical system of human physician interpretation with an automated machine learning classifier. The system extracts features from medical images and combines them with clinical data to generate risk scores, eliminating human subjectivity and variability in plaque vulnerability assessment while maintaining high prediction accuracy.
Solution Approach 2:
The patent introduces an intermediary machine learning classifier that mediates between raw medical imaging data and clinical decisions. This intermediary system processes multiple imaging modalities and clinical parameters, transforming them into standardized risk scores that provide consistent and objective plaque vulnerability assessment.
2Device complexity
If limited imaging information is used for plaque assessment, then the assessment process remains simple, but the accuracy of vulnerability prediction decreases
Solution Approach 1:
The patent segments the plaque assessment into distinct feature extraction components for different imaging modalities (anatomical, morphological, hemodynamic, biochemical). Each modality contributes specific features that are processed separately then integrated, allowing the system to handle complex multi-modal data while maintaining organizational simplicity through modular architecture.
Solution Approach 2:
The patent merges multiple imaging modalities and clinical data sources into a unified risk assessment framework. By combining anatomical features from CT, morphological features from OCT/IVUS, hemodynamic parameters, and biochemical markers, the system achieves superior prediction accuracy while presenting a consolidated output that simplifies clinical interpretation.
3Reliability
If multiple data sources are combined for plaque assessment, then the accuracy and consistency of vulnerability assessment improves, but the complexity of the assessment system increases
Solution Approach 1:
The patent transforms diverse data from multiple sources into standardized parameter formats that can be uniformly processed by the machine learning classifier. Clinical parameters, imaging features, and laboratory values are all converted to comparable data structures, enabling consistent integration while managing system complexity through standardized data representation.
Data Source
AI summary
Rather than rely on variation from physician to physician and limited imaging information for assessing plaque vulnerability of a patient, medical imaging and other information are used by a machine-implemented classifier to predict plaque rupture. Anatomical, morphological, hemodynamic, and biochemical features are used in combination to classify plaque.