Image-Based Plaque Analysis for High-Risk Lesion Stratification
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Solution Overview
Problem
Current diagnostic and treatment approaches for coronary plaque are inadequate, as they fail to accurately identify high-risk plaques and guide effective treatment plans, leading to potential misdiagnosis and ineffective interventions.
Innovation Solution
Systems and methods utilizing non-invasive image-based plaque analysis, including machine learning and artificial intelligence, to quantify and categorize plaque characteristics, such as distances, volumes, densities, and shapes, enabling precise identification of high-risk plaques and guiding personalized treatment plans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current diagnostic approaches are used for plaque identification, then the diagnostic process is simple, but the accuracy of identifying high-risk plaques is insufficient
Solution Approach 1:
The diagnostic system segments the plaque analysis into multiple components: image acquisition, image processing, feature extraction, and risk classification. Each component handles a specific aspect of the analysis, allowing the system to achieve high accuracy through specialized processing of different plaque characteristics such as morphology, density, and texture features.
Solution Approach 2:
The system introduces an intermediary image processing layer between raw image acquisition and final diagnosis. This intermediary layer includes normalization techniques and feature extraction algorithms that transform raw images into quantifiable parameters, enabling accurate risk stratification without requiring direct interpretation of complex medical images by clinicians.
2Adaptability or versatility
If traditional plaque analysis methods are used, then the analysis process is quick, but the ability to guide effective treatment plans is inadequate
Solution Approach 1:
The system performs preliminary risk stratification and treatment recommendation generation during the image analysis phase. By pre-calculating risk scores and treatment options based on extracted plaque features, the system provides treatment guidance concurrently with diagnosis, eliminating the need for separate analysis steps and reducing overall time loss.
Solution Approach 2:
The system utilizes multiple plaque parameters including density values, morphological measurements, and texture features to create a comprehensive risk profile. By analyzing changes in these parameters and comparing them against established thresholds, the system adapts treatment recommendations to match the specific characteristics of each patient's plaque burden and composition.
3Reliability
If invasive procedures are used for plaque assessment, then the diagnostic accuracy is high, but the patient risk and complexity increase
Solution Approach 1:
The system replaces mechanical invasive procedures with non-invasive imaging-based analysis. By substituting physical intervention with computational image processing and automated feature extraction, the system maintains diagnostic reliability while eliminating the harmful effects associated with invasive plaque assessment methods.
Solution Approach 2:
The system creates detailed digital copies of plaque characteristics through image processing. By generating virtual models and quantitative representations of plaque morphology, density, and composition from non-invasive images, the system achieves reliable diagnostic information without requiring physical contact or invasive sampling of the plaque tissue.
Data Source
AI summary
This application is directed to systems, methods, and devices for image based analysis of plaque. In some embodiments, the approaches herein can be used for developing treatment plans, which can include local treatment, systemic treatment, or both. In some embodiments, the approaches herein can be used for stent selection. In some embodiments, the approaches herein can be used for surgical planning, which can include robotic surgical planning. In some embodiments, the approaches herein can be used for image normalization. In some embodiments, the approaches herein can be used for identifying plaque calcification thresholds. In some embodiments, the approaches herein can be used for identifying thin cap fibroatheroma. In some embodiments, the approaches herein can be used for coronary artery tree reconstruction. Some embodiments are directed to coronary artery disease risk stratification.