Objectives Metabolic bariatric surgery is a critical intervention for patients living with obesity
and related health issues. Accurate classification and prediction of patient outcomes are vital …

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 …

Background The pilot study addresses the challenge of predicting postoperative outcomes,
particularly Body Mass Index (BMI) trajectories, following bariatric surgery. The complexity of …

Artificial intelligence (AI) is an umbrella term, which refers to different methods that simulate
the process of human learning. As is the case with medicine in general, the field of bariatric …

Artificial intelligence (AI) is a revolution in data analysis with emerging roles in various
specialties and with various applications. The objective of this scoping review was to retrieve …

Obesity is defined as a body mass index (BMI) of 30 kg/m 2 or greater. Obesity is a complex
medical condition characterized by excess body fat that increases the risk of developing co …

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 …

Abstract Objectives Laparoscopic metabolic surgery (MxS) can lead to remission of type 2
diabetes (T2D); however, treatment response to MxS can be heterogeneous. Here, we …

Background: Surgery is the only long-lasting effective treatment for morbid obesity.
Prediction on successful weight loss after surgery by data mining technologies is lacking …

Roux-en-Y gastric bypass (RYGB) is a treatment for severe obesity. However, many patients
have insufficient total weight loss (TWL) after RYGB. Although multiple factors have been …