Reducing prediction volatility in the surgical workflow recognition of endoscopic pituitary surgery.

Journal: International journal of computer assisted radiology and surgery
Published Date:

Abstract

PURPOSE: Workflow recognition can aid surgeons before an operation when used as a training tool, during an operation by increasing operating room efficiency, and after an operation in the completion of operation notes. Although several methods have been applied to this task, they have been tested on few surgical datasets. Therefore, their generalisability is not well tested, particularly for surgical approaches utilising smaller working spaces which are susceptible to occlusion and necessitate frequent withdrawal of the endoscope. This leads to rapidly changing predictions, which reduces the clinical confidence of the methods, and hence limits their suitability for clinical translation.

Authors

  • Adrito Das
    Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom. adrito.das.20@ucl.ac.uk.
  • Sophia Bano
    Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) and Department of Computer Science, University College London, London, UK. sophia.bano@ucl.ac.uk.
  • Francisco Vasconcelos
    Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London 43-45 Foley St, London, W1W 7TY, UK.; Department of Computer Science, University College London, 66-72 Gower St, London WC1E 6EA, UK.
  • Danyal Z Khan
    Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, University College, London, United Kingdom; Wellcome EPSRC Centre for Interventional and Surgical Sciences, University College, London, United Kingdom.
  • 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.
  • Danail Stoyanov
    University College London, London, UK.