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Artificial Intelligence in Bariatric Surgery: Current Status and Future Perspectives

2022; Springer Science+Business Media; Volume: 32; Issue: 8 Linguagem: Inglês

10.1007/s11695-022-06146-1

1708-0428

Mustafa Bektaş, Beata M. M. Reiber, Jaime Costa Pereira, George L. Burchell, Donald L. van der Peet,

Artificial Intelligence in Healthcare and Education

Abstract Background Machine learning (ML) has been successful in several fields of healthcare, however the use of ML within bariatric surgery seems to be limited. In this systematic review, an overview of ML applications within bariatric surgery is provided. Methods The databases PubMed, EMBASE, Cochrane, and Web of Science were searched for articles describing ML in bariatric surgery. The Cochrane risk of bias tool and the PROBAST tool were used to evaluate the methodological quality of included studies. Results The majority of applied ML algorithms predicted postoperative complications and weight loss with accuracies up to 98%. Conclusions In conclusion, ML algorithms have shown promising capabilities in the prediction of surgical outcomes after bariatric surgery. Nevertheless, the clinical introduction of ML is dependent upon the external validation of ML.