Novel multimodal sensing and machine learning strategies to classify cognitive workload in laparoscopic surgery.

Journal: European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
Published Date: Oct 15, 2024

Abstract

BACKGROUND: Surgeons can experience elevated cognitive workload (CWL) during surgery due to various factors including operative technicalities and the environmental demands of the operating theatre. This can result in poorer outcomes and have a detrimental effect on surgeon well-being. The objective measurement of CWL provides a potential solution to facilitate classification of workload levels, however results are variable when physiological measures are used in isolation. The aim of this study is to develop and propose a multimodal machine learning (ML) approach to classify CWL levels using a bespoke sensor platform and to develop a ML approach to impute missing pupil diameter measures due to the effect of blinking or noise.

Authors

  • Ravi Naik
    Hamlyn Centre for Robotic Surgery, Imperial College London, London, SW7 2AZ, UK; Department of Surgery and Cancer, St Mary's Hospital, Imperial College London, London, UK. Electronic address: ravi.naik15@imperial.ac.uk.
  • Adrian Rubio-Solis
    Hamlyn Centre for Robotic Surgery, Imperial College London, London, SW7 2AZ, UK; Department of Surgery and Cancer, St Mary's Hospital, Imperial College London, London, UK. Electronic address: arubioso@ic.ac.uk.
  • Kaizhe Jin
    Hamlyn Centre for Robotic Surgery, Imperial College London, London, SW7 2AZ, UK; Department of Surgery and Cancer, St Mary's Hospital, Imperial College London, London, UK. Electronic address: k.jin20@imperial.ac.uk.
  • George Mylonas
    Hamlyn Centre for Robotic Surgery, Imperial College London, London, SW7 2AZ, UK.

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