Continuous monitoring of surgical bimanual expertise using deep neural networks in virtual reality simulation
Recai Yilmaz, Alexander Winkler-Schwartz, Nykan Mirchi, Aiden Reich, Sommer Christie, Dan Huy Tran, Nicole Ledwos, Ali M Fazlollahi, Carlo Santaguida, Abdulrahman J Sabbagh, Khalid Bajunaid, Rolando Del Maestro
NPJ Digit Med . 2022 Apr 26;5(1):54. doi: 10.1038/s41746-022-00596-8.
In procedural-based medicine, the technical ability can be a critical determinant of patient outcomes. Psychomotor performance occurs in real-time, hence a continuous assessment is necessary to provide action-oriented feedback and error avoidance guidance. We outline a deep learning application, the Intelligent Continuous Expertise Monitoring System (ICEMS), to assess surgical bimanual performance at 0.2-s intervals. A long-short term memory network was built using neurosurgeon and student performance in 156 virtually simulated tumor resection tasks. Algorithm predictive ability was tested separately on 144 procedures by scoring the performance of neurosurgical trainees who are at different training stages. The ICEMS successfully differentiated between neurosurgeons, senior trainees, junior trainees, and students. Trainee average performance score correlated with the year of training in neurosurgery. Furthermore, coaching and risk assessment for critical metrics were demonstrated. This work presents a comprehensive technical skill monitoring system with predictive validation throughout surgical residency training, with the ability to detect errors.
Read Full Article Here: https://doi.org/10.1038/s41746-022-00596-8