3D Distance-Color-Coded Assessment of PCI Stent Apposition via Deep-Learning-Based Three-Dimensional Multi-Object...

Area of Science:

• Cardiovascular Imaging
• Artificial Intelligence in Medicine
• Medical Device Technology

Background:

• Coronary artery disease (CAD) is a major global health issue requiring percutaneous coronary intervention (PCI) with stent implantation.
• Effective stent apposition is critical to prevent complications like in-stent restenosis after PCI.
• Current assessment methods may lack the precision needed for optimal clinical evaluation.

Purpose of the Study:

• To introduce a novel 3D distance-color-coded assessment (DccA) for evaluating PCI stent apposition.
• To leverage deep learning for precise 3D segmentation of stents and vessel lumens in IV-OCT images.
• To enhance the visualization and clinical assessment of stent deployment.

Main Methods:

• Development of a deep-learning-based 3D multi-object segmentation model for IV-OCT images.
• Utilizing a hybrid-dimensional spatial matching network and dual-layer training with style transfer.
• Quantifying and mapping 3D stent-lumen distances into a color-coded space for visual assessment.

Main Results:

• Achieved over 95% segmentation precision for both stent struts and the vessel lumen.
• Successfully implemented 3D color visualization of stent-lumen distances.
• Demonstrated accurate 3D assessment of PCI stent apposition.

Conclusions:

• The proposed 3D DccA method offers a significant improvement in evaluating PCI stent deployment.
• Accurate segmentation and 3D visualization facilitate better clinical decision-making.
• This technology supports personalized treatment planning for patients undergoing PCI.