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Applications of Artificial Intelligence in Chronic Total Occlusion Revascularization: From Present to Future-A
Velina Doktorova1, Georgi Goranov1, Petar Nikolov1
1First Department of Internal Diseases, Section of Cardiology, Medical University of Plovdiv, 4000 Plovdiv, Bulgaria.
Insights
Artificial intelligence (AI) and machine learning (ML) significantly improve predictions for complex chronic total occlusion (CTO) percutaneous coronary intervention (PCI) procedures. These AI tools offer superior diagnostic and prognostic insights compared to traditional methods, paving the way for enhanced patient care.
Area of Science:
- Interventional Cardiology
- Artificial Intelligence in Medicine
- Machine Learning for Healthcare
Background:
- Chronic total occlusion (CTO) percutaneous coronary intervention (PCI) is a complex cardiology procedure with variable outcomes.
- Existing angiographic scores (J-CTO, PROGRESS-CTO) have limited predictive accuracy for CTO PCI.
- AI and machine learning (ML) offer potential for enhanced insights by integrating multimodal data.
Purpose of the Study:
- To review the applications of AI/ML in CTO PCI, focusing on diagnostic imaging, procedural planning, and prognostic modeling.
- To compare the predictive accuracy of AI/ML models against conventional scores and methods in CTO PCI.
Main Methods:
- Structured narrative review of literature from January 2010 to September 2025 using PubMed, Scopus, and Web of Science.
- Inclusion of peer-reviewed original research, reviews, and meta-analyses on AI/ML in CTO PCI.
- Qualitative synthesis of 33 selected studies from an initial screening of 330 records.
Main Results:
- AI in diagnostic imaging achieved high accuracy (AUC up to 0.87 for CTO detection) and segmentation reproducibility (>95%).
- ML algorithms outperformed traditional scores in procedural prediction (AUCs 0.73-0.82 vs. 0.62-0.70).
- AI prognostic models (CatBoost, neural networks) showed superior 5-year mortality prediction (AUCs 0.83-0.84), highlighting comorbidities and functional status as key predictors.
Conclusions:
- AI represents a paradigm shift in CTO PCI, offering superior accuracy over conventional risk models.
- AI enables more accurate, patient-centered risk prediction for CTO PCI.
- Further development in federated learning, multimodality integration, and explainable AI is crucial for clinical translation.
Abstract:
Background: Chronic total occlusion (CTO) percutaneous coronary intervention (PCI) remains among the most complex procedures in interventional cardiology, with variable technical success and heterogeneous long-term outcomes. Conventional angiographic scores such as J-CTO and PROGRESS-CTO provide only modest predictive accuracy and neglect critical patient and operator-related factors. Artificial intelligence (AI) and machine learning (ML) have emerged as transformative tools, capable of integrating multimodal data and offering enhanced diagnostic, procedural, and prognostic insights. Methods: We performed a structured narrative review of the literature between January 2010 and September 2025 using PubMed, Scopus, and Web of Science. Eligible studies were peer-reviewed original research, reviews, or meta-analyses addressing AI/ML applications in CTO PCI across imaging, procedural planning, and prognostic modeling. A total of 330 records were screened, and 33 studies met the inclusion criteria for qualitative synthesis. Results: AI applications in diagnostic imaging achieved high accuracy, with deep learning on coronary CT angiography yielding AUCs up to 0.87 for CTO detection, and IVUS/OCT segmentation demonstrating reproducibility > 95% compared with expert analysis. In procedural prediction, ML algorithms (XGBoost, LightGBM, CatBoost) outperformed traditional scores, achieving AUCs of 0.73-0.82 versus 0.62-0.70 for J-CTO/PROGRESS-CTO. Prognostic models, particularly CatBoost and neural networks, achieved AUCs of 0.83-0.84 for 5-year mortality in large registries (n ≈ 3200), surpassing regression-based methods. Importantly, comorbidities and functional status emerged as stronger predictors than procedural strategy. Future Directions: AI integration holds promise for real-time guidance in the catheterization laboratory, robotics-assisted PCI, federated learning to overcome data privacy barriers, and multimodality fusion incorporating imaging, clinical, and patient-reported outcomes. However, clinical adoption requires prospective multicenter validation, harmonization of endpoints, bias mitigation, and regulatory oversight. Conclusions: AI represents a paradigm shift in CTO PCI, providing superior accuracy over conventional risk models and enabling patient-centered risk prediction. With continued advances in federated learning, multimodality integration, and explainable AI, translation from research to routine practice appears within reach.
