Coronary Plaque Imaging for Non-Invasive CAD Risk Stratification
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Solution Overview
Problem
Current methods for non-invasive plaque analysis in coronary arteries lack comprehensive and accurate assessment of plaque characteristics, such as distance, volume, and morphology, which are crucial for determining the risk of coronary artery disease (CAD) and generating effective treatment plans.
Innovation Solution
Systems and methods for non-invasive image-based plaque analysis that include identifying low-density non-calcified plaque regions, determining distances to vessel walls, assessing plaque embeddedness and shape, and using machine learning algorithms to generate a risk assessment of CAD, based on medical images from techniques like CT, ultrasound, and MRI.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If invasive procedures like angioplasty and stent are performed, then treatment can be provided, but unnecessary invasive procedures occur due to inaccurate identification of unstable plaque
Solution Approach 1:
The system performs preliminary non-invasive CT imaging and plaque analysis before invasive procedures are considered. By pre-identifying unstable plaque characteristics (low radiodensity, specific morphology, embeddedness in vessel wall), the system enables clinicians to target invasive procedures only to patients with confirmed high-risk plaque, thereby avoiding unnecessary interventions in stable plaque cases.
Solution Approach 2:
The patent replaces invasive mechanical diagnostic procedures with non-invasive CT imaging and automated image analysis algorithms. The system uses computational methods to assess plaque stability based on radiodensity measurements and morphological features, substituting the need for invasive angiography and physical examination while improving diagnostic accuracy.
2Reliability
If blood tests and drug treatments are used, then treatment can be provided, but significant cardiovascular risk areas are missed
Solution Approach 1:
The system segments the coronary artery into distinct regions and characterizes plaque in each segment individually. By dividing the vasculature and analyzing plaque morphology, density, and location in each segment, the system identifies specific high-risk areas that would be missed by global blood tests, enabling targeted treatment of significant cardiovascular risk areas.
Solution Approach 2:
The patent introduces CT imaging and automated image analysis as an intermediary between blood tests and clinical decision-making. This intermediary provides direct visualization and quantitative assessment of plaque characteristics, bridging the gap between non-specific blood markers and specific anatomical risk areas, thereby preventing loss of critical risk information.
3Measurement precision
If non-invasive CT imaging and automated analysis are performed, then accurate plaque identification is achieved, but device complexity increases
Solution Approach 1:
The system employs automated image analysis algorithms that self-process the CT images without requiring manual measurement or interpretation. The software automatically segments plaque, calculates radiodensity, determines morphology, and generates risk assessments, making the complex analysis functions self-executing and reducing the operational burden on clinicians despite the underlying system complexity.
Solution Approach 2:
The patent transforms complex imaging data into simplified quantitative parameters (radiodensity values, plaque volume, morphology indices) that can be directly interpreted for clinical decision-making. By changing the representation of complex plaque characteristics into standardized numerical parameters, the system maintains measurement precision while making the output accessible and actionable for clinicians.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Provide a detailed and accurate risk assessment of CAD by analyzing plaque characteristics, enabling personalized treatment recommendations and improved diagnostic capabilities for cardiovascular diseases.
Implementation Method 1
analyzing the identified one or more regions of plaque to identify one or more regions of low density non-calcified plaque, non-calcified plaque, or calcified plaque based at least in part on density
Data Source
AI summary
Systems and methods of facilitating determination of risk of coronary artery disease (CAD) based at least in part on one or more measurements derived from non-invasive medical image analysis. The methods can include accessing a non-invasive generated medical image, identifying one or more arteries, identifying, regions of plaque within an artery, analyzing the regions of plaque to identify low density non-calcified plaque, non-calcified plaque, or calcified plaque based at least in part on density, determining a distance from identified regions of low density non-calcified plaque to one or more of a lumen wall or vessel wall, determining embeddedness of the regions of low density non-calcified plaque by one or more of non-calcified plaque or calcified plaque, determining a shape of the more regions of low density non-calcified plaque, and generating a display of the analysis to facilitate determination of one or more of a risk of CAD of the subject.