2-022 Artifact-corrected artificial intelligence-LED intracoronary oct analysis identifies plaque progression and vulnerability, drug efficacy and patient events

Introduction Intracoronary optical coherence tomography (OCT) can identify changes following drugs or devices and high-risk plaques causing major adverse cardiovascular events (MACE), but detailed OCT analysis requires time-consuming expert clinician or core laboratory analysis, whilst artifacts and limited sampling impair reproducibility. Assistive technologies such as artificial intelligence (AI)-based analysis may aid both interpretation and patient management, but many studies exhibit methodological, dataset, and reporting deficiencies that limit their robustness for clinical application. Methods We analysed 366 OCT pullbacks from 297 patients in two linked studies in patients with coronary artery disease. The OCT artifact burden that prevented accurate tissue identification and/or plaque measurements was determined using both ex-vivo (6,916 frames, 14 patients) and clinical trial OCT pullbacks (5,750 frames, 22 patients). 36,212 unselected frames (127 whole pullbacks, 106 patients) were used to design and validate a modular AI-based system that corrects artifact-containing OCT images and automatically identifies plaque composition and risk (AutoOCT), with outputs comprising labelled images and plaque component measurements. Validation of tissue and plaque classification used ex-vivo OCT pullbacks with co-registered histopathology, while external validation used core laboratory analysis of the high-intensity statin (HIS) IBIS-4 (83 baseline/follow-up patients) and natural history CLIMA (62 patients) studies respectively to determine if AI-based systems can detect plaque progression or regression, changes in plaque composition and higher-risk features with drugs, and future MACE. Results Autopsy and clinical pullbacks contained a similar and wide range of OCT artifacts in ~50% of frames, with ~10% preventing accurate measurement of plaque features. Pre-processing corrected >70% of common artifacts and improved sensitivity and accuracy to detect fibrous plaques and fibroatheroma. AutoOCT had a plaque classification accuracy of 83% vs. histology(figure 1)(table 1), equivalent to expert clinician readers, and replicated core laboratory plaque composition changes after HIS, including reduced lesion lipid arc (13.3° vs. 12.5°) and increased minimum fibrous cap thickness (FCT, 18.9µm vs. 24.4µm)(figure 2). AutoOCT also identified high-risk plaque features leading to MACE including minimal lumen area <3.5 mm², Lipid arc >180°, and FCT <75µm, comparable to the CLIMA core laboratory (table 2). Conclusions Artifact-corrected, AI-based analysis of intracoronary OCT allows rapid analysis of all available data, identifies tissue and plaque types, and measures features of plaque stabilisation and high-risk plaques. AI-based OCT analysis may assist real-time clinical interpretation and augment clinician or core laboratory analysis for trials of drug/device efficacy and identifying high-risk lesions.