Arun Nemani

Surgical motor skill differentiation via functional near infrared spectroscopy

This study proposes a method to objectively differentiate surgical motor skill by analyzing brain activation in the prefrontal cortex, supplementary motor area (SMA), and primary motor cortex (M1) using functional near infrared spectroscopy (fNIRS) while performing a bimanual surgical task. Results show that experts have a significant decrease (p<;0.05) in functional activation in the prefrontal cortex, SMA, and M1 compared to surgical novices for the physical trainer bimanual task. However, experts show a significant increase in functional activation in the prefrontal cortex and SMA compared to novices for the virtual trainer bimanual surgical task.

Monte Carlo Based Simulation of Sensitivity Curvature for Evaluating Optimal Probe Geometry

Probe geometry can significantly influence the performances of functional near-infrared spectroscopy applications. This in silico work presents a novel approach for probe placement optimization based on the minimization of sensitivity curvature.

Objective assessment of surgical skill transfer using non-invasive brain imaging

BackgroundPhysical and virtual surgical simulators are increasingly being used in training technical surgical skills. However, metrics such as completion time or subjective performance checklists often show poor correlation to transfer of skills into clinical settings. We hypothesize that non-invasive brain imaging can objectively differentiate and classify surgical skill transfer, with higher accuracy than established metrics, for subjects based on motor skill levels.Study design18 medical students at University at Buffalo were randomly assigned into control, physical surgical trainer, or virtual trainer groups. Training groups practiced a surgical technical task on respective simulators for 12 consecutive days. To measure skill transfer post-training, all subjects performed the technical task in an ex-vivo environment. Cortical activation was measured using functional near-infrared spectroscopy (fNIRS) in the prefrontal cortex, primary motor cortex, and supplementary motor area, due to their direct impact on motor skill learning.ResultsClassification between simulator trained and untrained subjects based on traditional metrics is poor, where misclassification errors range from 20 to 41%. Conversely, fNIRS metrics can successfully classify physical or virtual trained subjects from untrained subjects with misclassification errors of 2.2% and 8.9%, respectively. More importantly, untrained subjects are successfully classified from physical or virtual simulator trained subjects with misclassification errors of 2.7% and 9.1%, respectively.ConclusionfNIRS metrics are significantly more accurate than current established metrics in classifying different levels of surgical motor skill transfer. Our approach brings robustness, objectivity, and accuracy in validating the effectiveness of future surgical trainers in translating surgical skills to clinically relevant environments.

Assessing bimanual motor skills with optical neuroimaging

Optical neuroimaging differentiates and classifies surgical motor skill levels with higher accuracy than current methods. Measuring motor skill proficiency is critical for the certification of highly skilled individuals in numerous fields. However, conventional measures use subjective metrics that often cannot distinguish between expertise levels. We present an advanced optical neuroimaging methodology that can objectively and successfully classify subjects with different expertise levels associated with bimanual motor dexterity. The methodology was tested by assessing laparoscopic surgery skills within the framework of the fundamentals of a laparoscopic surgery program, which is a prerequisite for certification in general surgery. We demonstrate that optical-based metrics outperformed current metrics for surgical certification in classifying subjects with varying surgical expertise. Moreover, we report that optical neuroimaging allows for the successful classification of subjects during the acquisition of these skills.

Noninvasive Brain Imaging Demonstrates that Surgical Skills Transfer from Training Simulators to Ex Vivo Models

INTRODUCTION: Published research shows that surgical simulation trainers provide effective and safe ways to acquire technical surgical motor skills. However, few published studies conclude that these motor skills transfer from simulation to operative environments. The purpose of this study is to measure surgical motor skill transfer from training simulators to ex-vivo tissue models using brain activation and task performance metrics.

Determining optimal loading frequencies for bone formation on joints using Finite Element Methods

Osteoporosis has been widely known as a reoccurring issue with the increase of age. By understanding key mechanical properties of bone, researchers can better understand methods to induce bone growth as a possible method for osteoporosis reversal. This study uses image modeling to characterize the natural frequency of normal bone by using Finite Element Methods (FEM). Results indicate that the natural frequency for a mouse femur sample is 23.574 Hz (bending mode 1) along with a natural frequency of 12.085 Hz (bending mode 1) for a mouse tibia sample. Ultimately, this data may be used as target loading frequencies for joint loading to optimally enhance bone growth.