Non-invasive coronary plaque analysis via machine learning
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Solution Overview
Problem
Current methods for treating cardiovascular disease often involve invasive procedures that may not be the most effective for all patients, as they fail to accurately assess the health of arterial vessels beyond blood chemistry and imaging, leading to potential misidentification of high-risk plaque areas and increased risk of cardiovascular events.
Innovation Solution
Non-invasive image-based systems and methods using computer vision and machine learning to analyze coronary plaque, determining its characteristics, and assessing cardiovascular risk by analyzing images from CT scans, allowing for the generation of personalized treatment plans and risk assessments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If invasive procedures are used to assess arterial vessel health, then diagnostic accuracy may be improved, but patient risk and procedure complexity increase
Solution Approach 1:
The patent replaces invasive mechanical procedures (catheterization, angiography) with non-invasive imaging technologies (CT, MRI, ultrasound) combined with computational analysis. This substitution maintains diagnostic capability while eliminating the harmful effects of invasive procedures on patients.
Solution Approach 2:
The patent introduces computational models and image processing algorithms as intermediaries between non-invasive imaging data and diagnostic conclusions. These computational tools extract meaningful information from imaging data without requiring direct physical intervention in the patient's vascular system.
2Ease of operation
If blood chemistry analysis alone is used to assess cardiovascular risk, then simplicity is maintained, but diagnostic precision deteriorates
Solution Approach 1:
The patent merges multiple diagnostic modalities (blood chemistry, non-invasive imaging, computational analysis) into an integrated assessment system. This combination leverages the simplicity of blood tests while adding the diagnostic precision of image-based plaque characterization, creating a comprehensive evaluation approach.
3Reliability
If comprehensive plaque characterization is performed, then treatment effectiveness is improved, but analysis complexity increases
Solution Approach 1:
The patent segments the complex task of plaque assessment into distinct analytical components: image acquisition, plaque detection, characterization (composition, location, morphology), and risk stratification. This segmentation allows each component to be optimized independently while maintaining overall system reliability.
Solution Approach 2:
The patent transforms complex medical imaging data into simplified quantitative parameters and risk scores that can be easily interpreted and used for treatment decision-making. By changing the parameter representation from raw image data to standardized metrics, the system maintains comprehensive analysis capability while reducing operational complexity.
Data Source
AI summary
Various embodiments described herein relate to systems, devices, and methods for non-invasive image-based plaque analysis and risk determination. In particular, in some embodiments, the systems, devices, and methods described herein are related to analysis of one or more regions of plaque, such as for example coronary plaque, using non-invasively obtained images that can be analyzed using computer vision or machine learning to identify, diagnose, characterize, treat and/or track coronary artery disease.