A Rectal Cancer Surgery Dataset: Use of artificial intelligence to aid automation of error identification.

Journal: Scientific data
PMID: 39885139

Abstract

Minimally invasive surgery is complex and prone to variation not routinely objectively measured. We established an association between skills and patient outcomes. The evolving application of artificial intelligence techniques could assist intraoperative analysis. In this study, we analysed 77 rectal cancer operations' videos from a multicentre RCT that were recorded unedited and underwent blinded manual analysis using a validated, bespoke performance assessment tool (LapTMEpt) and the Objective Clinical Human Reliability Analysis (OCHRA). The OCHRA methodology involved segmentation of the 77 operations and manually annotating each case for the enacted errors and near misses. We provide a detailed description of the errors and near misses of over 380 hours of video analysis, containing 1377 errors. This dataset can inform machine learning to assist progress toward a fully automated, objective assessment of surgical skills.

Authors

  • Walaa Ghamrawi
    Division of Surgery & Interventional Science, Royal Free Hospital Campus, University College London, London, UK.
  • Nathan Curtis
    Department of General Surgey, Dorset County Hospital NHS Foundation Trust, Dorchester, UK.
  • Jialang Xu
    Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, UK.
  • Matt Boal
    The Griffin Institute, Northwick Park and St Mark's Hospital, Watford Road, London, HA13UJ, UK.
  • Evangelos Mazomenos
    Wellcome/EPSRC Centre for Interventional & Surgical Sciences, University College London, London, UK.
  • Eddie Edwards
  • Danail Stoyanov
    University College London, London, UK.
  • Nader Francis
    Division of Surgery and Interventional Sciences, University College London, Gower Street, London, WC1E 6BT, UK.

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