Machine learning and artificial intelligence for predicting short and long-term complications following metabolic bariatric surgery - a systematic review

Abstract

Background: Machine learning (ML) and other applications of artificial intelligence (AI) are revolutionizing medicine, particularly in the field of surgery. These models have the potential to outperform traditional predictive tools, aiding clinicians in decision making and enhancing operative safety through improved patient selection.

Methods: A systematic search was conducted across PubMed/MEDLINE and Google Scholar, guided by the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement, to identify studies employing ML and AI algorithms to predict postoperative complications following metabolic bariatric surgery (MBS). The search included primary studies published in English up to November 2024. The area under the receiver operating characteristic curve (AUROC) was used as a surrogate metric for algorithm performance, with values exceeding 0.8 considered clinically significant; however, studies were not excluded based on AUROC thresholds.

Results: The search identified 23 studies meeting the inclusion criteria. These were categorized into seven domains: general complications (8 studies, 34.8%), readmissions after MBS (4 studies, 17.4%), hemorrhage (1 study, 4.3%), leaks (1 study, 4.3%), venous thromboembolism (3 studies, 13.0%), nutritional deficiencies (4 studies, 17.4%), and miscellaneous complications such as gastroesophageal reflux disease, gallbladder disease, and major adverse cardiovascular events (MACE) (3 studies, 13.0%). The studies spanned from 2007 to 2024, with 87.0% (20/23) published in or after 2019. In total, 87 AI/ML algorithms were analyzed. While several studies reported AUROC values exceeding 0.7, the highest achieved was 0.94. However, most studies exhibited methodological limitations, including a lack of external validation and inadequate handling of imbalanced datasets, where complication events were markedly fewer than non-events.

Conclusions: While AI and ML approaches generally outperform traditional predictive models in forecasting postoperative complications following MBS, few algorithms demonstrated clinically significant performance with AUROC values above 0.8. Future research should adopt more rigorous methodologies and implement strategies to address imbalanced datasets, ensuring broader clinical applicability of AI/ML tools.

Keywords

Metabolic bariatric surgery / artificial intelligence / machine learning / postoperative complications / leak / hemorrhage / bleeding / thromboembolism

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