Investigating surgeon performance metrics as key predictors of robotic herniorrhaphy outcomes using iterative machine learning models: retrospective study.

Journal: BJS open
PMID: 39980088

Abstract

BACKGROUND: Robotic data streams allow for capture of objective performance indicators, providing the ability to quantify and analyse operator technique and movement in optimizing postoperative outcomes. This study provided proof-of-concept demonstration of how intraoperative surgeon-factors could influence post-robotic herniorrhaphy complications via machine learning analyses of objective performance indicators.

Authors

  • Thomas H Shin
    Division of General and Gastrointestinal Surgery, Brigham and Women's Hospital, Boston, Massachusetts, USA.
  • Abeselom Fanta
    Applied Research, Intuitive Surgical Inc., Peachtree City, GA, USA.
  • Mallory Shields
    Data and Analytics, Intuitive Surgical, Sunnyvale, CA.
  • Georges Kaoukabani
    Good Samaritan Medical Center, Brockton, Massachusetts, USA.
  • Fahri Gokcal
    Department of Surgery, Good Samaritan Medical Center, Tufts University School of Medicine, One Pearl Street, Brockton, MA, 02301, USA.
  • Xi Liu
    Collaborative Innovation Center of Radiological Medicine of Jiangsu Higher Education Institutions, Suzhou Medical College, Soochow University, Suzhou, Jiangsu 215123, China.
  • Ali Tavakkoli
    Division of General and Gastrointestinal Surgery, Brigham and Women's Hospital, Boston, Massachusetts, USA.
  • O Yusef Kudsi
    Division of General and Gastrointestinal Surgery, Brigham and Women's Hospital, Boston, Massachusetts, USA.

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