Evidence-based decision support for optimal laparoscopic suturing in gynecologic oncology

Abstract

This study focused on extracting and integrating expert surgeons' opinions into a quantitative decision-making model based on the best-worst method and used eight significant criteria, including postoperative complications, suturing time, and the surgeon's skill level. Experts' opinions were combined with the standard set data from CholecInstanceSeg. The model achieved an accuracy of 91.3% with a very low inconsistency ratio of 0.03, robustly outperforming the analytic hierarchy process and simple weighting methods. Moreover, model execution time improved by almost 50%. This study fills the gaps left by previous works by reducing ergonomic biases and increasing automation and overall system reliability in the decision-making process. The model provides a more efficient framework that can be further developed with artificial Intelligence (AI) for tailored surgical decision support systems, thus providing more accurate frameworks for suturing technique selection in oncological surgeries.

Keywords

best-worst method (BWM) / gynecologic oncology / laparoscopic suturing

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RIGHTS & PERMISSIONS

2025 The Author(s). Precision Medical Sciences published by John Wiley & Sons Australia, Ltd on behalf of Nanjing Medical University Affiliated Cancer Hospital & Jiangsu Cancer Hospital.