A real-time approach for surgical activity recognition and prediction based on transformer models in robot-assisted surgery.

Journal: International journal of computer assisted radiology and surgery
PMID: 39799528

Abstract

PURPOSE: This paper presents a deep learning approach to recognize and predict surgical activity in robot-assisted minimally invasive surgery (RAMIS). Our primary objective is to deploy the developed model for implementing a real-time surgical risk monitoring system within the realm of RAMIS.

Authors

  • Ketai Chen
    Advanced Medical Devices Laboratory, Kyushu University, Nishi-ku, Fukuoka, 819-0382, Japan.
  • D S V Bandara
    Bionics Laboratory, Department of Mechanical Engineering, University of Moratuwa, Katubedda, Sri Lanka. Electronic address: sanjayavipula@gmail.com.
  • Jumpei Arata

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