Reimagining chronic total occlusion management interventions: the role of artificial intelligence in imaging, planning, and procedural guidance.

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Abstract

Chronic Total Occlusions (CTOs) remain among the most complex lesions encountered in percutaneous coronary intervention (PCI), presenting significant technical and clinical challenges due to ambiguous vessel anatomy, lesion heterogeneity, and high operator variability. Although recent advancements in interventional techniques have improved success rates, procedural outcomes remain variable. The integration of Artificial Intelligence (AI) into CTO management offers the potential to optimize each stage of care, including lesion assessment, procedural planning, real-time intra-procedural support, and post-procedural outcome prediction. This review synthesizes current evidence on AI applications across the CTO care continuum, highlighting the role of deep learning in imaging modalities such as optical coherence tomography (OCT) and coronary computed tomography angiography (CCTA), as well as machine learning models such as XGBoost for procedural strategy and outcome forecasting. Commercial platforms including Ultreon OCT and HeartFlow FFRCT demonstrate early translational value, although validation in CTO-specific contexts remains limited. Ethical considerations such as algorithmic transparency, data generalizability, and clinician trust are also addressed, with attention to explainable AI methods such as SHAP and LIME. As AI technologies continue to advance, future research should prioritize the development of interpretable, clinically validated models and encourage multidisciplinary collaboration to support ethical integration into interventional cardiology and improve patient outcomes.

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