Design and Evaluation of Neurosurgical Training Simulator

Surgical simulators are becoming more important in surgical training. Consumer smartphone technology has improved to allow deployment of VR applications and are now being targeted for medical training simulators. A surgical simulator has been designed using a smartphone, Google cardboard 3D glasses, and the Leap Motion (LM) hand controller. Two expert and 16 novice users were tasked with completing the same pointing tasks using both the LM and the medical simulator NeuroTouch. The novice users had an accuracy of 0.2717 bits (SD 0.3899) and the experts had an accuracy of 0.0925 bits (SD 0.1210) while using the NeuroTouch. Novices and experts improved their accuracy to 0.3585 bits (SD 0.4474) and 0.4581 bits (SD 0.3501) while using the LM. There were some tracking problems with the AR display and LM. Users were intrigued by the AR display and most preferred the LM, as they found it to have better usability

Design and evaluation of an augmented reality simulator using leap motion

Advances in virtual and augmented reality (AR) are having an impact on the medical field in areas such as surgical simulation. Improvements to surgical simulation will provide students and residents with additional training and evaluation methods. This is particularly important for procedures such as the endoscopic third ventriculostomy (ETV), which residents perform regularly. Simulators such as NeuroTouch, have been designed to aid in training associated with this procedure. The authors have designed an affordable and easily accessible ETV simulator, and compare it with the existing NeuroTouch for its usability and training effectiveness. This simulator was developed using Unity, Vuforia and the leap motion (LM) for an AR environment. The participants, 16 novices and two expert neurosurgeons, were asked to complete 40 targeting tasks. Participants used the NeuroTouch tool or a virtual hand controlled by the LM to select the position and orientation for these tasks. The length of time to complete each task was recorded and the trajectory log files were used to calculate performance. The resulting data from the novices\u27 and experts\u27 speed and accuracy are compared, and they discuss the objective performance of training in terms of the speed and accuracy of targeting accuracy for each system

Leap Motion Performance in an Augmented Reality Workspace

Advances in mobile technology have enabled virtual reality (VR) and augmented reality (AR) systems to become more accessible and affordable. There are several devices that can be integrated with the mobile platform to make the applications more interactive, such as Leap Motion (LM). In this article, an AR environment has been designed that uses an Android smartphone with the LM. It has been evaluated for usability and accuracy by designing 15 sphere-targeting tasks that require the participants to use the LM to place the tip of a virtual index finger within the sphere. The task completion time and fingertip location were recorded, and the accuracy of the task was evaluated by calculating the distance between the fingertip location and the center of the sphere in three dimensions and each individual direction. Participants were the most accurate in the width and height directions, but there was a significant decrease in accuracy in the depth direction. Several participants experienced a decrease in task completion time as they progressed through the tasks, but half of the participants experienced tracking problems that increased their task completion times. Overall, the participants reported that the system was very intuitive and performed as designed; however, further improvements are needed

Augmented reality for neurosurgical guidance: An objective comparison of planning interface modalities

Numerous augmented reality image guidance tools have been evaluated under specific clinical criteria, but there is a lack of investigation into the broad effect on targeting ability and perception. In this paper, we evaluated performance of 18 subjects on a targeting task modeling ventriculostomy trajectory planning. Users targeted ellipsoids within a mannequin head using both an augmented reality interface and a traditional slice-based interface for planning. Users were significantly more accurate by several measures using augmented reality guidance, but were seen to have significant targeting bias; depth was underestimated by users with low targeting success. Our results further demonstrate the need for superior depth cues in augmented reality implementations while providing a framework for objective evaluation of augmented reality interfaces

Design and evaluation of an augmented reality simulator using leap motion

Advances in virtual and augmented reality (AR) are having an impact on the medical field in areas such as surgical simulation. Improvements to surgical simulation will provide students and residents with additional training and evaluation methods. This is particularly important for procedures such as the endoscopic third ventriculostomy (ETV), which residents perform regularly. Simulators such as NeuroTouch, have been designed to aid in training associated with this procedure. The authors have designed an affordable and easily accessible ETV simulator, and compare it with the existing NeuroTouch for its usability and training effectiveness. This simulator was developed using Unity, Vuforia and the leap motion (LM) for an AR environment. The participants, 16 novices and two expert neurosurgeons, were asked to complete 40 targeting tasks. Participants used the NeuroTouch tool or a virtual hand controlled by the LM to select the position and orientation for these tasks. The length of time to complete each task was recorded and the trajectory log files were used to calculate performance. The resulting data from the novices' and experts' speed and accuracy are compared, and they discuss the objective performance of training in terms of the speed and accuracy of targeting accuracy for each system