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April 27, 2026Langenbeck s Archives of Surgery0 citationsOpen Access

Predicting Operative Time in Robotic Ventral Hernia Repair Using Machine Learning

Predicting operative time in robotic ventral hernia repair using machine learning: a retrospective pilot study

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Authors

FBFrancesco BrucchiAGAlice GoriICIlia Van Campenhout

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Overview

Retrospective pilot study compares machine learning models for operative time prediction in robotic VHR, suggesting key predictors.

Key Points

  • The research aims to predict operative time for robotic ventral hernia repair using machine learning models and to identify crucial preoperative predictors.
  • Retrospective single-center cohort study at AZORG Hospital including 208 patients undergoing robotic VHR.
  • Three machine learning models (Random Forest, Gradient Boosting, Ridge Regression) were used to predict operative time.
  • Model performance was assessed using MAE, RMSE, and R² with 5-fold cross-validation and feature importance analyzed via SHAP.
  • Random Forest provided the best performance with MAE of 38.0 min, RMSE of 52.8 min, and R² of 0.22.
  • Hernia size, mesh position, and BMI were identified as the top predictors for operative time with respective importance scores of 30.7%, 45.0%, and 14.2%.
  • Current predictive accuracy is insufficient for clinical implementation, indicating the need for larger studies.

Cite This Study

Brucchi et al. (2026) studied this question.

synapsesocial.com/papers/69eefc6dfede9185760d3812https://doi.org/10.1007/s00423-026-04057-8
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