MedTrace Logo

OCT-based Machine Learning FFR for Predicting Post-PCI FFR

NCT06341361 · Status: NOT_YET_RECRUITING · Type: OBSERVATIONAL · Enrollment: 82

Last updated 2024-04-02

No results posted yet for this study

Summary

This study aims to compare the diagnostic accuracy of the fractional flow reserve (FFR) model derived by machine learning based on optical coherence tomography (OCT) exam after coronary artery stent implantation with the wire-based FFR.

Conditions

  • Tomography, Optical Coherence
  • Fractional Flow Reserve, Myocardial

Interventions

DIAGNOSTIC_TEST

OCT-based machine learning FFR

OCT-based machine learning FFR and wire-based FFR

Sponsors & Collaborators

  • Gangnam Severance Hospital

    collaborator OTHER

  • Severance Hospital

    collaborator OTHER

  • Yonsei University

    lead OTHER

Principal Investigators

  • Jung-Sun Kim, MD, PhD · Severance Cardiovascular Hospital, Yonsei University College of Medicine

Eligibility

Min Age
19 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2024-04-15
Primary Completion
2024-04-30
Completion
2025-10-15

More Related Trials

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06341361 on ClinicalTrials.gov