Real-Time multifaceted artificial intelligence vs In-Person instruction in teaching surgical technical skills: a randomized controlled trial.

Trainees develop surgical technical skills by learning from experts who provide context for successful task completion, identify potential risks, and guide correct instrument handling. This expert-guided training faces significant limitations in objectively assessing skills in real-time and tracking learning. It is unknown whether AI systems can effectively replicate nuanced real-time feedback, risk identification, and guidance in mastering surgical technical skills that expert instructors offer.

Roadmap for Developing Complex Virtual Reality Simulation Scenarios: Subpial Neurosurgical Tumor Resection Model.

Background: Advancement and evolution of current virtual reality (VR) surgical simulation technologies are integral to improve the available armamentarium of surgical skill education. This is especially important in high-risk surgical specialties. Such fields including neurosurgery are beginning to explore the utilization of virtual reality simulation in the assessment and training of psychomotor skills.

The development of a virtual simulator for training neurosurgeons to perform and perfect endoscopic endonasal transsphenoidal surgery.

Background: A virtual reality (VR) neurosurgical simulator with haptic feedback may provide the best model for training and perfecting surgical techniques for transsphenoidal approaches to the sella turcica and cranial base. Currently there are 2 commercially available simulators: NeuroTouch (Cranio and Endo) developed by the National Research Council of Canada in collaboration with surgeons at teaching hospitals in Canada, and the Immersive Touch. Work in progress on other simulators at additional institutions is currently unpublished.

Virtual reality simulator: demonstrated use in neurosurgical oncology.

Background: The overriding importance of patient safety, the complexity of surgical techniques, and the challenges associated with teaching surgical trainees in the operating room are all factors driving the need for innovative surgical simulation technologies.

Technical Development: Despite these issues, widespread use of virtual reality simulation technology in surgery has not been fully implemented, largely because of the technical complexities in developing clinically relevant and useful models. This article describes the successful use of the NeuroTouch neurosurgical simulator in the resection of a left frontal meningioma.

NeuroTouch: a physics-based virtual simulator for cranial microneurosurgery training.

Background: A virtual reality neurosurgery simulator with haptic feedback may help in the training and assessment of technical skills requiring the use of tactile and visual cues.

Objective: To develop a simulator for craniotomy-based procedures with haptic and graphics feedback for implementation by universities and hospitals in the neurosurgery training curriculum.

Methods: NeuroTouch was developed by a team of more than 50 experts from the National Research Council Canada in collaboration with surgeons from more than 20 teaching hospitals across Canada.

Surgical scissors extension adds the 7th axis of force feedback to the Freedom 6S.

A virtual reality surgical simulator ideally allows seamless transition between the real and virtual world. In that respect, all of a surgeon's motions and tools must be simulated. Until now researchers have been limited to using a pen-like tool in six degrees-of-freedom.

In-vivo validation of a stent implantation numerical model.

A large deformation finite element model for the patient-specific prediction of stent implantation is presented as a potential tool to assist clinicians. The intervention simulation includes the complete stent deployment under balloon inflation and deflation in the artery. This paper describes the proposed model and presents an in-vivo validation of the model using pre- and post-intervention data from a patient who underwent stent implantation.

Computer prediction of balloon angioplasty from artery imaging.

The success of angioplasty depends on a balance between two conflicting objectives: maximization of artery lumen patency and minimization of mechanical damage. A finite element model for the patient-specific prediction of angioplasty is proposed as a potential tool to assist clinicians. This paper describes the general methodology and the algorithm that computes device/artery friction work during balloon insertion and deployment.