Coronary Plaque CT Analysis for Risk-Stratified Treatment Decisions
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Solution Overview
Problem
Current treatments for cardiovascular disease, such as stents and bypass surgeries, may not be effective for all patients, particularly those with stable heart disease, and there is a need for better understanding of arterial vessel health to determine appropriate treatment plans.
Innovation Solution
Utilizing non-invasive medical imaging technologies, such as CT scans, combined with machine learning and artificial intelligence algorithms to analyze coronary arteries and plaque, and employing a normalization device to calibrate medical images for accurate analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If invasive surgical procedures such as angioplasty and stent placement are performed, then large blockages in arteries can be mechanically opened to increase blood flow, but the procedures carry surgical risks and may not be effective for all patients, particularly those with stable heart disease
Solution Approach 1:
The patent replaces invasive mechanical surgical procedures (angioplasty, stent placement) with non-invasive medical imaging and computational analysis systems. The system uses CT imaging combined with machine learning algorithms to analyze arterial health and predict treatment outcomes, substituting mechanical intervention with information-based decision support that identifies which patients truly need invasive procedures versus those who would benefit from conservative management
Solution Approach 2:
The patent introduces an intermediary computational system that acts as a mediator between diagnosis and treatment. This system processes imaging data, applies machine learning models to predict plaque stability and treatment response, and provides risk stratification that guides clinical decision-making, thereby reducing unnecessary invasive procedures while maintaining effectiveness for patients who truly need them
2Ease of operation
If physicians rely on macro-level biochemical analysis and angiography to assess cardiovascular health, then treatment decisions can be made based on blood cholesterol levels and visible blockages, but these methods fail to provide detailed understanding of arterial vessel health and plaque characteristics
Solution Approach 1:
The patent segments the arterial system into individual vessel segments and further into plaque components, analyzing each separately. The system divides the coronary artery tree into multiple segments, identifies plaque within each segment, and characterizes plaque composition (calcified, soft, mixed) and stability features individually, providing detailed information that macro-level analysis cannot capture
Solution Approach 2:
The patent adds a new dimension of analysis by combining three-dimensional CT imaging with machine learning-based plaque characterization. The system moves beyond two-dimensional angiographic views to volumetric assessment of arterial structures, enabling quantification of plaque burden, composition, and spatial distribution that provides comprehensive arterial health information
3Measurement precision
If machine learning and artificial intelligence algorithms are used to analyze medical images and identify plaque characteristics, then precise classification of stable vs. unstable plaque can be achieved, but the system complexity and computational requirements increase
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models on large datasets of annotated medical images before clinical use. The system performs offline training and validation to establish robust algorithms for plaque detection and classification, so that during actual clinical operation, the pre-trained models can rapidly analyze images with high accuracy without requiring complex real-time computations or extensive processing resources
Data Source
AI summary
The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and/or dynamically identify one or more features, such as plaque and vessels, and/or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and/or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and/or quantified parameters.