A deep learning approach for objective evaluation of microscopic neuro-drilling craniotomy skills.

Journal: Computers in biology and medicine
Published Date: Jul 11, 2025

Abstract

BACKGROUND: Minimally invasive microscopic and endoscopic neurosurgery demands precise use of high-speed micro-drilling tools to prevent potential complications. Present-day neuro-drilling training methods include cadaveric specimens and surgical simulators. However, skills assessment is mostly manual, and there is a pressing need for automation and personalized feedback for trainee surgeons. The lack of well-annotated datasets limits deep learning (DL)-based automation.

Authors

  • Raman Kumar
    Pieris Pharmaceuticals GmbH, Carl-Zeiss-Ring 15a, 85737, Ismaning, Germany.
  • Rohan Raju Dhanakshirur
    Amarnath and Shashi Khosla School of Information Technology, Indian Institute of Technology Delhi, New Delhi, India.
  • Ramandeep Singh
    Massachusetts General Hospital, Department of Radiolgoy, United States.
  • Ashish Suri
    Neuro-engineering Lab, Department of Neurosurgery, All India Institute of Medical Sciences, New Delhi, 110029, Delhi, India. Electronic address: surineuro@gmail.com.
  • Prem Kumar Kalra
    Department of Computer Science and Engineering, Indian Institute of Technology Delhi, New Delhi, 110016, Delhi, India. Electronic address: pkalra@cse.iitd.ac.in.
  • Chetan Arora
    Department of Computer Science and Engineering, Indian Institute of Technology, New Delhi, India.

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