Machine-Learning Coronary Image Analysis for Plaque-Based Diagnosis
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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 there is a need for a more accurate assessment of arterial vessel health to determine appropriate treatment strategies.
Innovation Solution
Utilizing non-invasive medical imaging technologies, such as CT scans, combined with machine learning algorithms and normalization devices, to analyze coronary arteries and plaque, generating personalized treatment plans and tracking disease progression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If invasive surgical procedures such as angioplasty and stent implantation are performed to treat cardiovascular disease, then immediate relief of severe blockages is achieved, but the procedures carry surgical risks and complications and may not be effective for all patient types
Solution Approach 1:
The system performs preliminary assessment of arterial health using non-invasive imaging and machine learning analysis before treatment decisions are made. This allows identification of patients who would benefit from invasive procedures versus those who would be better served by medication, preventing unnecessary surgical exposure to risks
Solution Approach 2:
The patent introduces an intermediate diagnostic system that uses machine learning algorithms to analyze medical images and provide objective assessment of arterial blockages. This intermediary system bridges the gap between traditional subjective clinical judgment and invasive treatment decisions, enabling more precise patient selection for surgery versus medication
2Ease of operation
If traditional macro-level biochemical analysis is used to assess cardiovascular health, then treatment decisions can be made based on blood markers, but the analysis lacks detailed information about specific arterial blockages and plaque characteristics
Solution Approach 1:
The system segments the assessment into multiple levels: macro-level biochemical markers remain useful for general cardiovascular risk, while micro-level machine learning analysis of medical images provides detailed information about specific arterial blockages, plaque composition, and vessel characteristics. This multi-scale segmentation preserves both simplicity and detail
Solution Approach 2:
The patent adds a new dimension to cardiovascular assessment by incorporating machine learning-based image analysis alongside traditional biochemical markers. This creates a multi-dimensional evaluation framework that captures both systemic cardiovascular risk and localized arterial pathology, enabling more informed treatment decisions
3Measurement precision
If machine learning algorithms are used to analyze medical images for detailed arterial assessment, then personalized treatment decisions can be made, but the complexity of the analysis system increases
Solution Approach 1:
The machine learning system performs self-training and self-optimization using large datasets of medical images and outcomes. Once trained, the algorithm autonomously analyzes new patient images without requiring manual calibration or adjustment, reducing the operational complexity despite the sophisticated underlying models
Solution Approach 2:
The patent replaces manual radiological assessment with automated machine learning algorithms that process medical images. This substitution eliminates the need for expert radiologist interpretation while maintaining or improving measurement precision, trading algorithmic complexity for reduced human labor complexity
4Reliability
If invasive angioplasty procedures are performed annually on large numbers of patients, then cardiovascular events can be prevented, but the procedures expose many patients to unnecessary surgical risks when medication might be sufficient
Solution Approach 1:
The system performs preliminary risk stratification using machine learning analysis of medical images to identify which patients truly need invasive intervention versus those who would be adequately treated with medication. This preliminary assessment prevents unnecessary exposure to surgical risks while ensuring appropriate patients receive definitive treatment
Solution Approach 2:
The patent changes the decision-making parameters from traditional binary choices (surgery vs. medication) to a continuous risk spectrum assessed by machine learning algorithms. This enables precise differentiation between patients requiring invasive procedures and those suitable for conservative management, optimizing the risk-benefit ratio across the patient population
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.