Automated Vision-Based Microsurgical Skill Analysis in Neurosurgery Using Deep Learning: Development and Preclinical Validation.

Journal: World neurosurgery
PMID: 33588081

Abstract

BACKGROUND/OBJECTIVE: Technical skill acquisition is an essential component of neurosurgical training. Educational theory suggests that optimal learning and improvement in performance depends on the provision of objective feedback. Therefore, the aim of this study was to develop a vision-based framework based on a novel representation of surgical tool motion and interactions capable of automated and objective assessment of microsurgical skill.

Authors

  • Joseph Davids
    Department of Surgery and Cancer, Hamlyn Centre for Robotic Surgery, Imperial College London, London, United Kingdom; Imperial College Healthcare NHS Trust, St. Mary's Praed St., Paddington, London, United Kingdom; Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom.
  • Savvas-George Makariou
    Department of Surgery and Cancer, Hamlyn Centre for Robotic Surgery, Imperial College London, London, United Kingdom.
  • Hutan Ashrafian
    Institute of Global Health Innovation, Imperial College London, London, United Kingdom.
  • Ara Darzi
    Imperial College London London UK.
  • Hani J Marcus
    The Hamlyn Centre for Robotic Surgery, Institute of Global Health Innovation, Imperial College London, Paterson Building (Level 3), Praed Street, London, W2 1NY, UK, hani.marcus10@imperial.ac.uk.
  • Stamatia Giannarou
    Hamlyn Centre of Robotic Surgery, Department of Surgery and Cancer Imperial College London London UK.

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