AI Coronary Plaque Imaging With Normalization-Based Risk Stratification
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Solution Overview
Problem
Current treatments for cardiovascular disease, such as stents and bypass surgeries, may not be effective for patients with stable heart disease, and existing methods like angiography and blood chemistry analysis fail to accurately identify high-risk plaque areas, leading to potential misdiagnosis and invasive procedures.
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 standardize image calibration, enabling accurate identification, quantification, and classification of plaque types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If non-invasive medical imaging technologies are used, then diagnostic accuracy is improved, but device complexity increases
Solution Approach 1:
A normalization device with known materials is introduced as an intermediary between the imaging system and the patient's coronary arteries. This mediator provides reference points that enable accurate quantification of plaque characteristics without requiring complex invasive procedures, thus improving diagnostic accuracy while managing system complexity.
Solution Approach 2:
The system changes measurement parameters by using dual-energy or spectral CT imaging to obtain different energy level images. This allows for automated quantification of plaque composition (calcium, soft plaque, fibrous tissue) through parameter transformation, improving diagnostic precision without proportionally increasing device complexity.
2Measurement precision
If machine learning and AI algorithms are used to analyze medical images, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The machine learning and AI algorithms perform self-service by automatically analyzing the normalized medical images to identify and characterize plaque without requiring manual interpretation by physicians. The system autonomously quantifies plaque composition and generates diagnostic information, improving measurement precision while the complexity is contained within the automated processing layer.
3Measurement precision
If normalization devices with multiple compartments are used, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The normalization device is segmented into multiple compartments, each containing a different known material (calcium, soft plaque, fibrous tissue). This segmentation allows for simultaneous calibration of different plaque types in a single imaging session, improving measurement precision across multiple plaque characteristics while organizing complexity into manageable discrete units.
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.