Deep learning for predicting cardiac procedure outcomes: A scoping review of recent advances

Abstract

Accurate prediction of outcomes after cardiac procedures is critical for personalised decision-making and risk stratification. While machine learning (ML) has shown promise in this domain, most prior studies rely on traditional ML methods that require structured data and manual feature engineering, limiting scalability. Many deep learning (DL) architectures offer an alternative by enabling automated feature extraction, particularly from unstructured data such as text, images, and signals. This scoping review summarises recent advances in DL-based prediction of outcomes for four major cardiovascular procedures: percutaneous coronary intervention (PCI), coronary artery bypass grafting (CABG), aortic valve replacement (AVR), and mitral valvuloplasty. Following PRISMA-ScR guidelines, we searched PubMed and IEEE Xplore for studies published between 2020 and March 2025. Finally, 457 studies were retrieved and nine eligible studies were included after screening. DL models demonstrated varying performance across data types, with particularly strong results for text and imaging tasks. Multimodal approaches combining clinical, imaging, and signal data showed added predictive value. Compared with traditional ML, DL models often reduce the need for manual feature engineering, though they still require preprocessing and validation to mitigate overfitting. Overall, these findings suggest that DL has potential to support preoperative risk stratification, although evidence for clinical utility remains preliminary. Moreover, all included studies lacked external validation, and challenges remain regarding generalisability, explainability, and integration into clinical workflows. Future research should prioritise large, diverse cohorts, multimodal data fusion, and interpretable DL models to enable safe and effective clinical implementation.