Observational studyEuropean heart journal2025
Artificial intelligence-based identification of thin-cap fibroatheromas and clinical outcomes: the PECTUS-AI study.
Observational study in European heart journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The trial behind it
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Who cites it
4 citing papers in PubMed.
- From Electrocardiography to the Catheterization Laboratory: A Multimodal Artificial Intelligence Framework for Acute Coronary Syndrome Detection and Risk Stratification.Diagnostics (Basel, Switzerland) · 2026Review
- A Clinician-Oriented Approach to Plaque Pathology in ACS: Implications for Personalized Cardiovascular Medicine-A Comprehensive Review.Journal of personalized medicine · 2026Review
- Intracoronary Imaging for the Management of Vulnerable Plaques.Journal of clinical medicine · 2026Review
- Advances in the Diagnosis and Management of High-Risk Cardiovascular Conditions: Biomarkers, Intracoronary Imaging, Artificial Intelligence, and Novel Anticoagulants.Journal of cardiovascular development and disease · 2026Review
Corrections and comments
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Authors and funding
15 authors.
Funding
Abstract
BACKGROUND AND
aimsCoronary 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.
methodsThe 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.
resultsAmong 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%).
conclusionsAI-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.
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