Skip to main content

Research timeline

Related research and updates

Public articles linked to the same research event.

Journal of the American Heart Association

Real-World External Validation of Artificial Intelligence-Based Full-Vessel Segmentation for Intracoronary Optical Coherence Tomography

This retrospective, single-center external validation study enrolled 100 consecutive patients undergoing clinically indicated OCT and used the previously developed OCT-AID algorithm to perform automated pixelwise full-vessel labeling of 2560 analyzable frames, comparing it frame by frame against an expert manual reference standard; agreement was excellent for calcified plaque identification (κ=0.88) and quantification (intraclass correlation coefficients 0.79–0.93), close to interobserver variability, reasonable for lipid plaque identification and quantification (κ=0.68; lipid arc intraclass correlation coefficient 0.79; minimum fibrous cap thickness intraclass correlation coefficient 0.