Abstract
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.