A Deep Learning Approach to Classify Surgical Skill in Microsurgery Using Force Data from a Novel Sensorised Surgical Glove.

Journal: Sensors (Basel, Switzerland)
Published Date:

Abstract

Microsurgery serves as the foundation for numerous operative procedures. Given its highly technical nature, the assessment of surgical skill becomes an essential component of clinical practice and microsurgery education. The interaction forces between surgical tools and tissues play a pivotal role in surgical success, making them a valuable indicator of surgical skill. In this study, we employ six distinct deep learning architectures (LSTM, GRU, Bi-LSTM, CLDNN, TCN, Transformer) specifically designed for the classification of surgical skill levels. We use force data obtained from a novel sensorized surgical glove utilized during a microsurgical task. To enhance the performance of our models, we propose six data augmentation techniques. The proposed frameworks are accompanied by a comprehensive analysis, both quantitative and qualitative, including experiments conducted with two cross-validation schemes and interpretable visualizations of the network's decision-making process. Our experimental results show that CLDNN and TCN are the top-performing models, achieving impressive accuracy rates of 96.16% and 97.45%, respectively. This not only underscores the effectiveness of our proposed architectures, but also serves as compelling evidence that the force data obtained through the sensorized surgical glove contains valuable information regarding surgical skill.

Authors

  • Jialang Xu
    Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, UK.
  • Dimitrios Anastasiou
    Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, UK.
  • James Booker
    Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, UK.
  • Oliver E Burton
    Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, UK.
  • Hugo Layard Horsfall
    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. Electronic address: Hugo.layardhorsfall@ucl.ac.uk.
  • Carmen Salvadores Fernandez
    Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, UK.
  • Yang Xue
    State Key Laboratory of Oral and Maxillofacial Reconstruction and Regeneration, National Clinical Research Center for Oral Diseases, Shaanxi Clinical Research Center for Oral Diseases, Department of Oral and Maxillofacial Surgery, School of Stomatology, The Fourth Military Medical University, Xi'an, China.
  • Danail Stoyanov
    University College London, London, UK.
  • Manish K Tiwari
    Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, 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.
  • Evangelos B Mazomenos
    Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, UK.