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Enhancing percutaneous coronary intervention using TriVOCTNet: a multi-task deep learning model for comprehensive

Yu Shi Lau1, Li Kuo Tan2, Kok Han Chee3

  • 1Faculty of Engineering, Department of Biomedical Engineering, Universiti Malaya, Kuala Lumpur, Malaysia.

Physical and Engineering Sciences in Medicine
|January 6, 2025
PubMed
Summary

A new deep learning model, TriVOCTNet, automates analysis of intravascular optical coherence tomography (IVOCT) images for percutaneous coronary intervention (PCI). It accurately segments lumen and struts for both metal stents and bioresorbable vascular scaffolds (BVS) in a single pass.

Keywords:
Deep learningIntravascular optical coherence tomographyLumen segmentationNeointimal coverageStent appositionStent struts segmentation

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Area of Science:

  • Cardiovascular Imaging
  • Medical Artificial Intelligence
  • Interventional Cardiology

Background:

  • Intravascular optical coherence tomography (IVOCT) is vital for assessing percutaneous coronary intervention (PCI) outcomes, specifically neointimal coverage and stent apposition.
  • Current automated analysis tools for IVOCT struggle with diverse stent types (metal, bioresorbable vascular scaffold/BVS) and lack end-to-end automation for entire image pullbacks.
  • This limits the practical application of automated IVOCT analysis in complex PCI scenarios.

Purpose of the Study:

  • To develop a unified deep learning algorithm for automated analysis of IVOCT images across various PCI phases and stent types.
  • To enable intelligent selection and segmentation of relevant pullback segments within a single, efficient system.
  • To address the limitations of existing methods by handling both metal stents and BVS concurrently.

Main Methods:

  • A multi-task deep learning model, TriVOCTNet, was proposed, integrating image classification, lumen segmentation, and stent strut segmentation.
  • The network performs classification/selection, lumen segmentation, and both metal and BVS stent strut segmentation in a single pass.
  • A specialized joint loss function was designed to optimize all tasks, even when certain elements (e.g., specific stent types) are absent.

Main Results:

  • TriVOCTNet achieved high classification accuracies: 0.999 for lumen, 0.997 for BVS, and 0.998 for metal stents.
  • Lumen segmentation demonstrated a Euclidean distance error of 21.72 μm and a Dice coefficient of 0.985.
  • Stent strut segmentation yielded a Dice coefficient of 0.896 for BVS and precision/sensitivity of 0.895/0.868 for metal stents.

Conclusions:

  • TriVOCTNet offers a simplified, single-system solution for comprehensive IVOCT analysis in PCI.
  • The model demonstrates robust performance across different stent types and clinical scenarios, including the presence of both metal and BVS.
  • Its speed, accuracy, and versatility highlight significant clinical potential for optimizing PCI assessment.