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Machine learning-based preoperative analytics for the prediction of anastomotic leakage in colorectal surgery: a swiss pilot study.

Stephanie Taha-MehlitzLarissa WentzlerFiorenzo AngehrnAhmad HendieVincent OchsJulia WollebVictor E StaartjesBassey EnodienMartinas BaltuonisStephan VorburgerDaniel M FreyRobert RosenbergMarkus von FlüeBeat Müller-StichPhilippe C CattinAnas TahaDaniel Steinemann
Published in: Surgical endoscopy (2024)
In this pilot study, we evaluated ML-based prediction models for AL post-colorectal surgery and identified ten patient-related risk factors associated with AL. Highlighting the need for multicenter data, external validation, and larger sample sizes, our findings emphasize the potential of ML in enhancing surgical outcomes and inform future development of a web-based application for broader clinical use.
Keyphrases
  • big data
  • machine learning
  • artificial intelligence
  • rectal cancer
  • patients undergoing
  • case report
  • deep learning
  • double blind
  • drug induced