AI Medical Image Simulation for PCI Stent Under-Expansion Risk
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional risk assessment methods for stent under-expansion during PCI procedures, such as IVUS and OCT imaging, increase procedure duration and cost while posing additional patient risk.
Innovation Solution
An AI-based risk assessment system using machine learning models analyzes preoperative and intraoperative medical images to determine the risk of stent under-expansion, optimizing stent deployment through simulations and iterative cost function optimization, and providing clinical decision support.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If IVUS/OCT imaging is used to assess stent under-expansion risk, then measurement precision is improved, but procedure duration increases
Solution Approach 1:
The system performs preliminary risk assessment using machine learning models on preoperative medical images (CT, MRI, or X-ray) to identify patients at risk of stent under-expansion before the PCI procedure begins. This advance assessment eliminates the need for routine IVUS/OCT imaging during the procedure, reducing procedure duration while maintaining accurate risk identification.
Solution Approach 2:
The system creates a virtual 3D model of the coronary artery and stent from preoperative medical images, simulating the stent expansion process computationally. This virtual copy allows for accurate assessment of stent expansion without requiring physical IVUS/OCT imaging during the actual procedure.
2Measurement precision
If IVUS/OCT imaging is used to assess stent under-expansion risk, then measurement precision is improved, but procedure cost increases
Solution Approach 1:
The system performs risk assessment using readily available preoperative medical images and machine learning models before the PCI procedure. This eliminates the need for expensive IVUS/OCT imaging during the procedure, significantly reducing procedure costs while maintaining accurate risk assessment capability.
Solution Approach 2:
The system uses computational algorithms and software-based simulation instead of expensive hardware-based IVUS/OCT imaging systems. The virtual 3D modeling and machine learning assessment provide comparable diagnostic value at a fraction of the cost.
3Measurement precision
If IVUS/OCT imaging is used to assess stent under-expansion risk, then measurement precision is improved, but patient risk increases
Solution Approach 1:
The system performs risk assessment using non-invasive preoperative medical images (CT, MRI, or X-ray) to identify patients at risk of stent under-expansion before the PCI procedure. This eliminates the need for additional invasive IVUS/OCT imaging during the procedure, reducing patient exposure to procedural risks while maintaining accurate risk identification.
Solution Approach 2:
The system uses virtual 3D modeling to simulate stent expansion and assess risk without requiring physical intrusion into the coronary artery. This computational approach eliminates the risks associated with additional catheter manipulation and contrast injection required for IVUS/OCT imaging.
4Productivity
If machine learning risk assessment and simulation are performed, then productivity is improved, but device complexity increases
Solution Approach 1:
The system integrates multiple functions into a single unified platform: preoperative risk assessment using machine learning, virtual 3D model generation, stent expansion simulation, and clinical decision support. This multi-functional integration improves productivity by providing comprehensive assessment in one system while managing complexity through unified architecture.
Solution Approach 2:
The system introduces a computational intermediary layer that processes preoperative medical images and generates risk assessments and simulations. This software-based intermediary bridges the gap between existing medical imaging infrastructure and clinical decision-making, improving productivity without requiring complex hardware modifications.
Data Source
AI summary
Systems and methods for clinical decision support are provided. One or more input medical images of an anatomical object of a patient are received. Characteristics of the anatomical object are extracted from the one or more input medical images. A risk associated with a medical procedure to be performed on the anatomical object is determined based on the one or more input medical images and the extracted characteristics of the anatomical object using one or more machine learning based risk assessment models. A simulation of the medical procedure is performed on the anatomical object based on the one or more input medical images, the extracted characteristics of the anatomical object, and the risk associated with the medical procedure. One or more clinical decisions associated with the medical procedure are automatically made based on the risk associated with the medical procedure and results of the simulation. The one or more clinical decisions are output.