Automated segmentation and clinical characterization of layered plaque in intracoronary optical coherence tomography.

Background: Layered plaque, representing organized thrombus overlying a previously disrupted plaque, is a marker of coronary destabilization and plaque vulnerability. Despite its clinical relevance, identifying layered plaque remains expert dependent with interobserver variability, limiting large-scale assessment. We developed and validated an artificial intelligence (AI) model for layered plaque segmentation, and assessed occurrence and associations with plaque vulnerability in non-culprit lesions. Methods: Optical coherence tomography (OCT) pullbacks from the PECTUS-obs and ORANGE datasets were used to develop an enhanced version of our validated multiclass segmentation model (OCT-AID), by adding layered plaque as an additional pixel-level class. Pullbacks were divided into a training set ( Results: The model achieved a Dice coefficient of 0.68 ± 0.27 for layered plaque segmentation, with pullback-level sensitivity of 96.2% and specificity of 75.0%. At least one layered plaque was present in 366/414 patients (88.4%). Presence of layered plaque was higher in STEMI patients ( Conclusions: Automated OCT-based identification of layered plaque using AI is feasible. In non-culprit coronary lesions, layered plaque was highly prevalent and associated with established markers of plaque vulnerability.