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Challenges in multi-centric generalization: phase and step recognition in Roux-en-Y gastric bypass surgery.

Joël L LavanchySanat RameshDiego Dall'AlbaCristians GonzalezPaolo FioriniBeat P Müller-StichPhilipp C NettJacques MarescauxDidier MutterNicolas Padoy
Published in: International journal of computer assisted radiology and surgery (2024)
MultiBypass140 shows considerable variation in surgical technique and workflow of LRYGB procedures between centers. Therefore, generalization experiments demonstrate a remarkable difference in model performance. These results highlight the importance of multi-centric datasets for AI model generalization to account for variance in surgical technique and workflows. The dataset and code are publicly available at https://github.com/CAMMA-public/MultiBypass140.
Keyphrases
  • roux en y gastric bypass
  • weight loss
  • gastric bypass
  • minimally invasive
  • healthcare
  • mental health
  • machine learning
  • rna seq
  • single cell
  • surgical site infection
  • deep learning