Artificial intelligence-based identification of thin-cap fibroatheromas and clinical outcomes: the PECTUS-AI study
Rick Volleberg, Thijs Luttikholt, Ruben van der Waerden, Pierandrea Cancian, Joske van der Zande, Xiaojin Gu, Jan‐Quinten Mol, Tomasz Roleder, Mathias Prokop, Clara I. Sánchez, Bram van Ginneken, Ivana Išgum, Simone Saitta, Jos Thannhauser, Niels van Royen
Abstract
BACKGROUND AND AIMS: Coronary thin-cap fibroatheromas (TCFA) are associated with adverse outcome, but identification of TCFA requires expertise and is highly time-demanding. This study evaluated the utility of artificial intelligence (AI) for TCFA identification in relation to clinical outcome. METHODS: The PECTUS-AI study is a secondary analysis from the prospective observational PECTUS-obs study, in which 438 patients with myocardial infarction underwent optical coherence tomography (OCT) of all fractional flow reserve-negative non-culprit lesions (i.e. target lesions). OCT images were analyzed for the presence of TCFA by an independent core laboratory (CL-TCFA) and OCT-AID, a recently developed and validated AI segmentation algorithm (AI-TCFA). The primary outcome was defined as the composite of death from any cause, non-fatal myocardial infarction or unplanned revascularisation at 2 years (±30 days), excluding procedural and stent-related events. RESULTS: Among 414 patients, AI-TCFA and CL-TCFA were identified in 143 (34.5%) and 124 (30.0%) patients, respectively. AI-TCFA within the target lesion was significantly associated with the primary outcome [hazard ratio (HR) 1.99, 95% confidence interval (CI) 1.02-3.90, P = .04], while the HR for CL-TCFA was non-significant (1.67, 95% CI: .84-3.30, P = .14). When evaluating the complete pullback, AI-TCFA showed an even stronger association with the primary outcome (HR 5.50, 95% CI: 1.94-15.62, P < .001; negative predictive value 97.6%, 95% CI: 94.0%-99.3%). CONCLUSIONS: AI-based OCT image analysis allows standardized identification of patients at increased risk of adverse cardiovascular outcome, offering an alternative to manual image analysis. Furthermore, AI-assisted evaluation of complete imaged segments results in better prognostic discrimatory value than evaluation of the target lesion only.