aceDevelopment of an Explainable Machine Learning Model for Predicting Reflux Esophagitis Among Candidates for Metabolic Bariatric Surgery
Background Reflux esophagitis (RE) is a common gastroesophageal disorder among candidates for metabolic bariatric surgery (MBS). This study aimed to develop and internally validate an explainable machine learning (ML) model for predicting preoperative RE risk among patients undergoing MBS. Methods This retrospective study included 1,068 MBS candidates who were randomly divided into training and internal test cohorts (7:3). Feature selection was performed using correlation analysis, multicollinearity assessment,…
An AI model using six routine pre-surgery measures predicted reflux esophagitis in 1,068 weight-loss surgery candidates, scoring an AUC of 0.825 on internal test data.
Model was only internally validated in a single retrospective cohort; external validation is still required to assess robustness and generalizability.
Evidence
- Peer-reviewedObesity Surgery2026-09-17
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Truvace Impact Record TRV-2026-1128, v2: “Development of an Explainable Machine Learning Model for Predicting Reflux Esophagitis Among Candidates for Metabolic Bariatric Surgery.” Truvace, 2026-11-04. /record/TRV-2026-1128 (accessed at citation time). sha256 dd8ce2b144414c19…
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