Ali M. Fazlollahi
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Ali M. Fazlollahi has a total of 30 co-authors. Here are the top co-authors by number of shared publications:
- Recai Yilmaz
Division of Neurosurgery and Pediatrics, Children’s National Medical Center, Washington, DC, USA
3 shared publications Carlo Santaguida
Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
2 shared publicationsDan Huy Tran
Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology & Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
2 shared publications- Abdulmajeed Albeloushi
Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada; Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
1 shared publication Abdulrahman J. Sabbagh
Division of Neurosurgery, Department of Surgery, College of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
1 shared publicationAbicumaran Uthamacumaran
Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
1 shared publication
Latest Publications
- Open Access
- 9 October 2025
- Published by Elsevier in Journal of Surgical Education
- Vol. 82 (11) , 103743
Effect of Artificial Intelligence-Augmented Human Instruction on Feedback Frequency and Surgical Performance During Simulation Training
- 9 October 2025
- Published by Elsevier in Journal of Surgical Education
- Vol. 82 (11) , 103743
Open AccessEffect of Artificial Intelligence-Augmented Human Instruction on Feedback Frequency and Surgical Performance During Simulation Training
- 29 December 2023
- Published by Elsevier in Journal of Surgical Education
- Vol. 81 (2) , 275-287
Effect of Feedback Modality on Simulated Surgical Skills Learning Using Automated Educational Systems– A Four-Arm Randomized Control Trial
- 29 December 2023
- Published by Elsevier in Journal of Surgical Education
- Vol. 81 (2) , 275-287
Effect of Feedback Modality on Simulated Surgical Skills Learning Using Automated Educational Systems– A Four-Arm Randomized Control Trial
- Open Access
- 26 April 2022
- Published by Springer Nature in npj Digital Medicine
- Vol. 5 (1) , 1-9
Continuous monitoring of surgical bimanual expertise using deep neural networks in virtual reality simulation
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.
- 26 April 2022
- Published by Springer Nature in npj Digital Medicine
- Vol. 5 (1) , 1-9
Open AccessContinuous monitoring of surgical bimanual expertise using deep neural networks in virtual reality simulation
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.