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Operative Time Prediction by Machine Learning for Robot-Assisted Laparoscopic Radical Prostatectomy.

Yu Suzuki1,2, Shinya Sonobe2,3,4, Yoshihide Kawasaki1

  • 1Department of Urology, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan.

International Journal of Urology : Official Journal of the Japanese Urological Association
|November 30, 2025
PubMed
Summary

This study developed a reliable operative time prediction system for robot-assisted radical prostatectomy (RARP). The system accurately predicts surgical duration, improving operating room scheduling efficiency.

Keywords:
SHAP valueslearning curvemachine learningoperative time predictionrobot‐assisted laparoscopic radical prostatectomy

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Area of Science:

  • Urology
  • Surgical Oncology
  • Data Science

Background:

  • Efficient operating room scheduling is vital for healthcare systems.
  • Accurate prediction of operative time for robot-assisted radical prostatectomy (RARP) remains a challenge.
  • Optimizing surgical workflow requires precise time estimations.

Purpose of the Study:

  • To develop and validate an operative time prediction system for RARP.
  • To enhance the accuracy of predicting surgical duration in robotic prostatectomy.
  • To provide a tool for improving operating room scheduling efficiency.

Main Methods:

  • Retrospective analysis of 557 (Tohoku University Hospital) and 150 (Miyagi Cancer Center) RARP patients.
  • Collected variables included patient demographics, comorbidities, tumor characteristics, and surgical factors.
  • Developed an integrated prediction system using approximation curves and a random forest machine learning model.

Main Results:

  • Achieved a normalized root mean square error of 0.107 (internal) and 0.148 (external) validation.
  • Demonstrated superior reliability compared to operator-based time estimations.
  • Identified key predictive factors: lymphadenectomy, grade group, prostate volume, and body mass index.

Conclusions:

  • The developed system offers reliable and robust operative time predictions for RARP.
  • The system effectively incorporates known factors influencing surgical duration.
  • Potential to significantly improve operating room scheduling and resource management.