Related Experiment Video
Updated: Sep 19, 2026
Optical Coherence Tomography Based Biomechanical Fluid-Structure Interaction Analysis of Coronary Atherosclerosis Progression
Published on: January 15, 2022
Real-World External Validation of Artificial Intelligence-Based Full-Vessel Segmentation for Intracoronary Optical
Rick H J A Volleberg1,2, Doosup Shin1, Ruben G A van der Waerden2,3
1Department of Cardiology St. Francis Hospital & Heart Center Roslyn NY USA.
Background:
Artificial intelligence (AI) allows automated evaluation of intracoronary optical coherence tomography (OCT) images. However, algorithms are mostly developed and validated on well-curated data sets, which may not represent real-world data. We sought to externally validate a previously developed algorithm performing full-vessel segmentation for OCT in an unselected consecutive real-world data set.
Methods:
This was a retrospective, single-center, external validation study comprising 100 consecutive patients undergoing clinically indicated OCT. A previously developed AI algorithm (OCT-AID) was used for automated pixelwise labeling of OCT images, distinguishing among lumen, guidewire artifact, intima, media, lipid plaque, calcium plaque, side branch, plaque rupture, thrombus, microvessel, and background. The AI-based predictions were compared on a frame level to the reference standard obtained through manual OCT image analysis by expert readers.
Results:
Among 2560 analyzable frames, the agreement between the automated OCT image analysis and the reference standard was excellent for calcified plaque identification (κ=0.88 [95% CI, 0.84-0.92]) and quantification (intraclass correlation coefficient values ranged between 0.79 and 0.93), with a performance close to interobserver variability. For lipid plaque identification and quantification, the model performance was reasonable (κ=0.68 [95% CI, 0.64-0.72]; intraclass correlation coefficient for lipid arc, 0.79 [95% CI, 0.76-0.81]; intraclass correlation coefficient for minimum fibrous cap thickness, 0.59 [95% CI, 0.55-0.63]) and largely superior to interobserver variability. The algorithm performance for low-prevalence features (e.g., plaque rupture) was limited.
Conclusions:
AI-based fully automated evaluation of OCT images is feasible with performances consistent with interobserver variability in a real-world data set of consecutive patients, supporting generalizability of the proposed methodology.