The development of machine learning in bariatric surgery - Enodien, Bassey; Taha-Mehlitz, Stephanie; Saad, Baraa; Nasser, Maya; Frey, Daniel M.; Taha, Anas (Frontiers in Surgery, 2023)
Background: Machine learning (ML), is an approach to data analysis that makes the process of analytical model building automatic. The significance of ML stems from its potential to evaluate big data and achieve quicker and more accurate outcomes. Bariatric surgery, otherwise referred to as weight loss surgery, reflects the series of procedures performed on people demonstrating obesity. This systematic scoping review aims to explore the development of ML in bariatric surgery. Methods: The study used the Preferred Reporting Items for Systematic and Meta-analyses for Scoping Review. A comprehensive literature search was performed of several databases including PubMed, Cochrane, and IEEE, and search engines namely Google Scholar. The PRESS checklist was used to evaluate the consistency demonstrated during the process. Results: A total of seventeen articles qualified for inclusion in the study. Out of the included studies, sixteen concentrated on the role of ML in prediction, while one addressed ML's diagnostic capacity. Most articles (n = 15) were journal publications, whereas the rest (n = 2) were papers from conference proceedings. Most included reports were from the United States (n = 6). Most studies addressed neural networks, with convolutional neural networks as the most prevalent. Also, the data type used in most articles (n = 13) was derived from hospital databases, with very few articles (n = 4) collecting original data via observation. Conclusions: This study indicates that ML has numerous benefits in bariatric surgery, however its current application is limited. Further large multicenter studies are required to validate results.