Journal
NPJ DIGITAL MEDICINE
Volume 5, Issue 1, Pages -Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41746-022-00596-8
Keywords
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Funding
- National Neurosciences Institute, King Fahad Medical City, Riyadh, Saudi Arabia
- Franco Di Giovanni Foundation
- Montreal English School Board
- Montreal Neurological Institute and Hospital
- Brain Tumour Foundation of Canada Brain Tumour Research Grant
- Fonds de recherche du Quebec-Sante
- Robert Maudsley Fellowship for Studies in Medical Education from the Royal College of Physicians and Surgeons of Canada
- Mitacs Grant
- Christian Gaeda Brain Tumour Research Studentship from the Montreal Neurological Institute at McGill University
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Technical ability plays a crucial role in procedural-based medicine, and continuous assessment of psychomotor performance is essential. This study introduces a deep learning application, the Intelligent Continuous Expertise Monitoring System (ICEMS), which can assess surgical bimanual performance with high frequency intervals and successfully differentiate between different levels of trainees.
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.
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