Surgical Errors
Surgical errors occur during operation. The following video demonstrates an error due to mishandling of the tool during tumor resection which leads to damage to the normal brain and bleeding: youtube.com/watch?v=S5p1Y72E_fQ
Project I: Effect of Artificial Intelligence (AI)-Augmented Human Instruction on Surgical Simulation Performance
This randomized clinical trial aims to evaluate the effect of AI-augmented personalized expert instruction—where human surgical educators are provided with quantitative AI performance data—compared with AI tutor instruction on trainees’ surgical performance, technical skill acquisition, and skill transfer during simulation training.
Project II: Impact of Instructional Intervention on Feedback Frequency Following AI Error Detection During Surgical Simulation
Using the NeuroVR neurosurgical simulation platform, this study investigates the effect of AI-augmented expert instruction on the number of instructions that a trainee receives, as well as on various surgical technical performance metrics.
Project III: Construct Validity of Neurosurgical Performance in Cerebral Corticectomy Using an Ex Vivo Calf Brain Simulation Model.
This study evaluates the effectiveness of an ex vivo calf brain simulation model for training in subpial corticectomy. The goals are to assess the model’s construct validity, identify key skills distinguishing expert surgeons from trainees, and track learning progress over time.
Project IV: Development of a Novel Ex-vivo Calf brain model for Force Detection during Neurosurgical Procedures.
This project develops a novel ex-vivo model in collaboration with biomedical engineers for force detection during neurosurgical procedures. This involves the creation of a highly sensitive system capable of accurately measuring the forces exerted on brain tissue during surgical manipulation. By integrating advanced sensor technologies, the system provides dynamic real-time data on force application, allowing for the analysis of surgical technique.
Project V: Developing an Intelligent Continuous Expertise Monitoring System (ICEMS) for Ex vivo Calf Brain Corticectomy.
This project aims to build an ex vivo intelligent tutoring system to enhance patient safety in surgical education by guiding trainees through complex subpial resection. The system provides real-time, personalized feedback and skill acquisition assessment using a Long Short-Term Memory (LSTM)-based model trained to analyze temporal patterns in surgical tool movements and force dynamics to predict deviations from expert performance.