AI Image-Based Plaque Analysis for Coronary Risk Stratification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current diagnostic and treatment approaches for coronary plaque are inadequate, failing to accurately identify high-risk plaques and guide effective treatment strategies, leading to potential misdiagnosis and ineffective interventions.
Innovation Solution
The use of image-based plaque analysis systems and methods, incorporating machine learning and artificial intelligence, to quantify and categorize plaque characteristics, such as distances, volumes, densities, and morphologies, enabling precise identification of high-risk plaques and guiding personalized treatment plans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional diagnostic approaches are used for coronary plaque assessment, then the diagnostic process is simple and quick, but the accuracy of identifying high-risk plaques is insufficient
Solution Approach 1:
The diagnostic system segments the coronary plaque assessment into multiple independent analysis components: morphological analysis (shape, size, location), compositional analysis (calcified, fibrous, lipid components), and risk stratification modules. Each segment processes specific features and combines results to achieve comprehensive accurate diagnosis while maintaining modularity that manages system complexity.
Solution Approach 2:
The system transitions from traditional 2D angiographic views to 3D volumetric reconstruction of coronary plaques using multiple imaging modalities. This dimensional enhancement allows assessment of plaque burden, composition, and spatial relationships that cannot be evaluated in conventional 2D images, significantly improving risk identification accuracy.
2Reliability
If comprehensive plaque analysis is performed to improve treatment guidance, then treatment accuracy is improved, but the analysis time and computational resources increase
Solution Approach 1:
The system performs preliminary processing of imaging data during the acquisition phase, pre-segmenting anatomical structures and calculating basic morphometric parameters before detailed analysis. This preliminary action reduces the computational burden during the actual diagnostic analysis, enabling comprehensive plaque characterization without proportionally increasing total analysis time.
Solution Approach 2:
The system implements iterative feedback loops where initial analysis results guide subsequent focused examination. High-risk features identified in preliminary screening trigger more detailed localized analysis, while low-risk areas receive streamlined assessment. This feedback-driven approach optimizes the balance between comprehensive analysis and time efficiency.
Data Source
AI summary
This application is directed to systems, methods, and devices for image based analysis of plaque. In some embodiments, the approaches herein can be used for developing treatment plans, which can include local treatment, systemic treatment, or both. In some embodiments, the approaches herein can be used for stent selection. In some embodiments, the approaches herein can be used for surgical planning, which can include robotic surgical planning. In some embodiments, the approaches herein can be used for image normalization. In some embodiments, the approaches herein can be used for identifying plaque calcification thresholds. In some embodiments, the approaches herein can be used for identifying thin cap fibroatheroma. In some embodiments, the approaches herein can be used for coronary artery tree reconstruction. Some embodiments are directed to coronary artery disease risk stratification.