Deep Learning-Based Assessment of Coronary Artery Morphologies for Predicting Responsiveness to Intravascular...

Area of Science:

• Cardiovascular Interventions
• Medical Imaging Analysis
• Artificial Intelligence in Medicine

Background:

• Intravascular lithotripsy (IVL) effectively expands lumens by modifying calcification.
• Predicting IVL effectiveness across diverse coronary morphologies requires further understanding.

Purpose of the Study:

• To identify predictive lumen and calcium morphologies for luminal expansion after IVL.
• To leverage artificial intelligence-enhanced image analysis for improved prediction.

Main Methods:

• Analysis of optical coherence tomography (OCT) data from 134 patients before IVL and after stenting.
• Application of a deep-learning approach to evaluate pre-IVL morphological features.
• Linear regression analysis to assess the impact of morphological predictors.

Main Results:

• AI-enhanced analysis significantly improved prediction of luminal expansion (adjusted R² increased from ~0.58 to ~0.72).
• Key predictors included lumen/calcium circularity, vessel wall eccentricity, and calcium-to-lumen balance.
• Procedural factors like stent diameter and IVL catheter size also correlated with expansion.

Conclusions:

• Deep learning models enhance IVL outcome prediction by incorporating complex preprocedural morphology.
• This AI-driven approach supports personalized procedural planning for intravascular lithotripsy.
• Prospective studies are needed to validate generalizability and clinical impact.