Coronary Microcalcification Prediction From Angiography and Hemodynamics
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Solution Overview
Problem
Current methods for assessing plaque stability in coronary arteries, such as OCT and 18F-NaF PET, are time-consuming, require expert analysis, and lack automated tools for biomechanical stress analysis, leading to inefficiencies and exposure to radiation.
Innovation Solution
A method using anatomical, image, and hemodynamic measurements to predict microcalcification activity in arteries, employing machine learning models to quantify plaque features and calculate biomechanical stresses, enabling automated analysis and reducing the need for invasive imaging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If 18F-NaF PET imaging is used to assess microcalcification activity, then measurement precision is improved, but loss of time and productivity deteriorate due to time-consuming procedures
Solution Approach 1:
The patent creates a computational model that copies the functionality of 18F-NaF PET imaging by using machine learning algorithms to predict microcalcification activity from standard coronary angiography images. This virtual copy eliminates the need for actual PET imaging while maintaining diagnostic accuracy, thereby resolving the time consumption issue without sacrificing measurement precision
Solution Approach 2:
The patent replaces the physical PET imaging system with an information processing system based on machine learning. Instead of using radioactive tracers and specialized imaging equipment, the system uses computational models trained on angiography images to predict microcalcification activity, substituting mechanical/physical imaging methods with digital information processing
2Measurement precision
If expert analysis is used for plaque stability assessment, then measurement precision is improved, but device complexity and ease of operation worsen due to requirement for specialized expertise
Solution Approach 1:
The patent implements a self-service system where the machine learning model automatically performs plaque stability assessment without requiring expert intervention. The system autonomously analyzes coronary angiography images, extracts relevant features, and generates diagnostic predictions, eliminating the need for specialized expert analysis while maintaining assessment accuracy
Solution Approach 2:
The patent introduces an intermediary machine learning system that acts as a bridge between standard coronary angiography and expert-level plaque stability assessment. This intermediary model translates routine imaging data into expert-quality diagnostic information, removing the dependency on specialized expertise while preserving measurement precision
3Measurement precision
If invasive imaging methods are used, then measurement precision is improved, but object-affected harmful factors increase due to radiation exposure
Solution Approach 1:
The patent creates a virtual imaging system that copies the diagnostic capabilities of invasive PET imaging using only standard coronary angiography. By training machine learning models on paired datasets of angiography and PET images, the system learns to predict microcalcification activity from non-invasive angiography images, eliminating radiation exposure while maintaining measurement precision
Solution Approach 2:
The patent converts the limitation of standard angiography (lack of microcalcification detail) into a benefit by using it as training data for machine learning models. The model learns to infer microcalcification activity from the angiography images, transforming a potentially harmful invasive procedure into a beneficial non-invasive diagnostic approach
4Productivity
If automated analysis tools are developed, then productivity is improved, but manufacturing precision and measurement precision may deteriorate without expert validation
Solution Approach 1:
The patent performs preliminary action by training the machine learning model extensively on large datasets of paired angiography and PET images before deployment. This pre-training phase captures expert-level diagnostic patterns, embedding measurement precision into the automated system itself, so that the automated tool maintains high accuracy without requiring real-time expert validation
Solution Approach 2:
The patent incorporates feedback mechanisms where the machine learning model's predictions can be validated against actual PET imaging results when available. This feedback loop allows continuous refinement and verification of measurement precision, ensuring that automated analysis maintains diagnostic accuracy while achieving high productivity
Data Source
AI summary
Systems and methods of predicting microcalcification activity in a vascular vessel comprising either an artery or a vein, comprising the steps of: (a) measuring patient data comprising one or more of: the existence of and/or quantity of coronary plaques or visible markers of disease in a vascular tissue sample; the existence of and/or quantity of healthy tissue in the vascular tissue sample; one or more features that define an abnormal hemodynamic environment in a vessel; one or more geometric features that are associated with vascular remodeling and which influence hemodynamics in a vessel, and/or one or more material properties that influence vascular hemodynamics; and (b) calculating the microcalcification activity in the vessel as a function of the measurements taken in Step (a).