Stent Expansion Prediction From OCT Calcification Imaging
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Solution Overview
Problem
Stent under-expansion due to calcification lesions in arteries leads to complications such as stent thrombosis and in-stent restenosis, which existing technologies struggle to predict accurately, making it difficult to determine effective treatment plans.
Innovation Solution
A fully automated method using machine learning to analyze pre-stent intravascular optical coherence tomography images, segmenting the lumen and calcification lesions, extracting features, and applying a regression model to predict a minimum stent expansion metric, classifying the vessel as under-expanded or well-expanded to guide treatment plans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If stent is implanted in calcified artery, then stent expansion is limited, but prediction accuracy of stent expansion is poor
Solution Approach 1:
The system performs preliminary analysis of intravascular images before stent implantation to predict expansion outcomes. By analyzing calcification characteristics and vessel morphology in advance, the system identifies patients at risk of under-expansion before the procedure, allowing for preventive planning rather than reactive correction.
Solution Approach 2:
The system incorporates feedback loops where predicted expansion metrics are compared with actual outcomes from previous procedures. This feedback mechanism continuously refines the prediction algorithms, improving accuracy over time by learning from real-world performance data and adjusting predictive models accordingly.
2Productivity
If manual assessment of stent expansion is used, then treatment planning is time-consuming, but automated prediction may reduce accuracy
Solution Approach 1:
The system introduces an intermediary automated analysis layer that processes intravascular images and extracts relevant features. This intermediary system bridges the gap between raw imaging data and clinical decision-making, providing structured predictions that assist rather than replace physician judgment, thereby maintaining accuracy while improving efficiency.
Solution Approach 2:
The patent replaces manual visual assessment with an automated machine learning system that analyzes intravascular images. This substitution eliminates the time-consuming nature of manual review while maintaining or improving accuracy through consistent, objective algorithmic analysis of calcification patterns and vessel characteristics.
3Measurement precision
If comprehensive feature extraction is performed, then prediction accuracy improves, but computational complexity increases
Solution Approach 1:
The system segments the analysis into distinct feature categories: calcification characteristics (area, thickness, distribution), vessel morphology (lumen area, reference diameter), and lesion properties. This segmentation allows the complex analysis to be broken into manageable components, each processed by specialized algorithms, reducing overall computational complexity while maintaining comprehensive analysis.
Solution Approach 2:
The system extracts only the most relevant features from intravascular images for prediction, rather than processing all possible image data. By identifying and extracting key calcification and vessel features that have the strongest correlation with stent expansion outcomes, the system reduces computational burden while preserving prediction accuracy.
Data Source
AI summary
The present disclosure, in some embodiments, relates to a method of predicting stent expansion. The method includes accessing a pre-stent intravascular image of a blood vessel of a patient and segmenting the pre-stent intravascular image to identify a lumen and a calcification lesion. A plurality of features are extracted from one or more of the lumen and the calcification lesion. A regression model is applied to one or more of the plurality of features to determine a minimum stent expansion metric (mSEM). The mSEM indicating how much a stent will expand after implantation. The mSEM is used to generate a classification of the blood vessel as an under-expanded area or a well-expanded area.