Automated segmentation of phases, steps, and tasks in laparoscopic cholecystectomy using deep learning.

Journal: Surgical endoscopy
PMID: 37945709

Abstract

BACKGROUND: Video-based review is paramount for operative performance assessment but can be laborious when performed manually. Hierarchical Task Analysis (HTA) is a well-known method that divides any procedure into phases, steps, and tasks. HTA requires large datasets of videos with consistent definitions at each level. Our aim was to develop an AI model for automated segmentation of phases, steps, and tasks for laparoscopic cholecystectomy videos using a standardized HTA.

Authors

  • Shruti R Hegde
    Department of Surgery, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX, 75390-9159, USA.
  • Babak Namazi
    Baylor Scott & White Research Institute, Dallas, TX, USA.
  • Niyenth Iyengar
    Department of Surgery, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX, 75390-9159, USA.
  • Sarah Cao
    Department of Surgery, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX, 75390-9159, USA.
  • Alexis Desir
    Department of Surgery, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX, 75390-9159, USA.
  • Carolina Marques
    Department of Surgery, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX, 75390-9159, USA.
  • Heidi Mahnken
    Department of Surgery, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX, 75390-9159, USA.
  • Ryan P Dumas
    Department of Surgery, Division, Burn, Trauma, Acute and Critical Care Surgery, Parkland Memorial Hospital/UT Southwestern Medical Center, Dallas, TX.
  • Ganesh Sankaranarayanan
    Department of Surgery, Baylor University Medical Center, 3500 Gaston Ave, Dallas, TX, 75246, USA. ganesh.sankaranarayanan@bswhealth.org.

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