Machine learning models to predict success of endoscopic sleeve gastroplasty using total and excess weight loss percent achievement: a multicentre study.

Fiche publication

Date publication

novembre 2023

Journal

Surgical endoscopy

Auteurs

Membres identifiés du Cancéropôle Est :
Pr MARESCAUX Jacques

Tous les auteurs :
Vannucci M, Niyishaka P, Collins T, Rodríguez-Luna MR, Mascagni P, Hostettler A, Marescaux J, Perretta S

Lien Pubmed

https://www.ncbi.nlm.nih.gov/pubmed/37973639

Résumé

The large amount of heterogeneous data collected in surgical/endoscopic practice calls for data-driven approaches as machine learning (ML) models. The aim of this study was to develop ML models to predict endoscopic sleeve gastroplasty (ESG) efficacy at 12 months defined by total weight loss (TWL) % and excess weight loss (EWL) % achievement. Multicentre data were used to enhance generalizability: evaluate consistency among different center of ESG practice and assess reproducibility of the models and possible clinical application. Models were designed to be dynamic and integrate follow-up clinical data into more accurate predictions, possibly assisting management and decision-making.