Deep learning prediction of error and skill in robotic prostatectomy suturing.

Journal: Surgical endoscopy
PMID: 39433583

Abstract

BACKGROUND: Manual objective assessment of skill and errors in minimally invasive surgery have been validated with correlation to surgical expertise and patient outcomes. However, assessment and error annotation can be subjective and are time-consuming processes, often precluding their use. Recent years have seen the development of artificial intelligence models to work towards automating the process to allow reduction of errors and truly objective assessment. This study aimed to validate surgical skill rating and error annotations in suturing gestures to inform the development and evaluation of AI models.

Authors

  • N Sirajudeen
    Wellcome/ESPRC Centre for Interventional Surgical Sciences (WEISS), University College London (UCL), London, UK.
  • M Boal
    The Griffin Institute, Northwick Park and St Marks Hospital, London, UK.
  • D Anastasiou
    Wellcome/ESPRC Centre for Interventional Surgical Sciences (WEISS), University College London (UCL), London, UK.
  • J Xu
    The Affiliated Wenling Hospital of Wenzhou Medial University, Wenling, China.
  • D Stoyanov
    Division of Engineering, University College London, London, UK.
  • J Kelly
    Division of Surgery and Interventional Science, Research Department of Targeted Intervention, UCL, London, UK.
  • J W Collins
    Division of Surgery and Interventional Science, Research Department of Targeted Intervention, UCL, London, UK.
  • A Sridhar
    Division of Surgery and Interventional Science, Research Department of Targeted Intervention, UCL, London, UK.
  • E Mazomenos
    Wellcome/ESPRC Centre for Interventional Surgical Sciences (WEISS), University College London (UCL), London, UK.
  • N K Francis
    Department of General Surgery, Yeovil District Hospital, Higher Kingston, Yeovil, BA21 4AT, UK, nader.francis@ydh.nhs.uk.

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