NCT06544681

Evaluation and Treatment Strategy Development of Coronary Heart Disease Guided by OCT Based on Multimodal Deep Learning

Evaluation and Treatment Strategy Development of Coronary Heart Disease Guided by Optical Coherence Tomography Based on Multimodal Deep Learning

• Status: Not Yet Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 2,000 (estimated)
• Sponsor: Xiang Ma · Academic / Other
• Sex: All
• Age: 20 Years – 80 Years
• Healthy volunteers: Not accepted

Summary

This is a retrospective, multicenter, observational study aimed at assessing stent apposition for coronary stent implantation by an optical coherence tomography system constructed by deep learning algorithms and evaluating the prognosis of patients after stent implantation in conjunction with multimodal diagnostic and therapeutic information.

Detailed description

In this study, we planned to retrospectively collect 2,000 subjects who underwent optical coherence tomography-guided percutaneous coronary stent implantation with an optical coherence tomography system constructed by a deep learning algorithm from 3 centers to assess stent apposition for coronary stent implantation, and to classify subjects into a group with poor stent apposition (axial distance \>400 μm or length \>1 mm) and a group with good stent apposition. All subjects were followed up within 12 months after the procedure.

Conditions

• Coronary Disease
• Coronary Artery Disease

Interventions

Type	Name	Description
PROCEDURE	Coronary stenting with planned drug eluting stent (DES).	Stenting will be performed with OCT guidance according to the algorithm described in the protocol. A deep learning-based OCT system was used to measure the adherence of coronary stents.

Timeline

• Start date: 2024-08-20
• Primary completion: 2026-12-31
• Completion: 2026-12-31
• First posted: 2024-08-09
• Last updated: 2024-08-09