This study aims to compare the cognitive workload levels of general surgery residents by measuring prefrontal cortex hemodynamic activity while performing a similar task using robotic-assisted surgery (RAS) and laparoscopic surgery simulators. The study was conducted with 22 general surgery residents who completed a peg transfer task in simulated laparoscopic and RAS training environments. Participants' performance and neurophysiological data were collected over a 1-month period. The study was conducted at Acibadem Mehmet Ali Aydinlar University- CASE (Center of Advanced Simulation and Education), utilizing a laparoscopic training simulator (CAE Lap VR) and robotic surgery simulator (Da Vinci Surgical System Si console with Backpack). Twenty-two general surgery residents (mean [SD] age, 29.45 [2.40] years; 18 [81.82%] male) volunteered for the study. None of the participants had prior experience with RAS or RAS simulators, whereas most had varying degrees of laparoscopic surgery experience. Significant differences were observed between RAS and laparoscopic simulations in terms of performance time and neural activity. Peg transfer times were shorter in RAS simulations compared to laparoscopic simulations (χ2(3) = 134.805, p < 0.001). Mean oxygenated hemoglobin (ΔHbO) levels in the prefrontal cortex were lower in RAS simulations (χ2(3) = 20.695, p < 0.001), indicating reduced cognitive workload. Relative Neural Efficiency (RNE) and Relative Neural Involvement (RNI) scores were higher in RAS tasks (χ2(1) = 55.765, p < 0.001), suggesting greater efficiency and involvement during robotic-assisted procedures. The findings indicate that RAS tasks are associated with lower cognitive workload and improved efficiency compared to laparoscopic tasks. Incorporating neural indices alongside performance metrics may enhance training assessments and provide deeper insights into trainees' experiences in simulation-based surgical education.

Background: Minimally invasive surgical techniques, including laparoscopic and robotic surgery, have profoundly impacted surgical practice by improving precision, reducing recovery times, and minimizing complications. However, these modalities differ in their cognitive demands and skill acquisition requirements, which can influence the learning curve and operative performance. To assess and evaluate this variability across these modalities, a functional near-infrared spectroscopy (fNIRS) system is used as an objective method for monitoring cognitive activity in surgical trainees. fNIRS can provide insights and further our understanding of the mental demands of different surgical techniques and their association with varying task complexity. Objective: This study seeks to assess the influence of surgical modality (laparoscopy vs. robotic surgery) and task complexity (pick and place (PP) vs. knot tying (KT)) on cognitive workload through fNIRS. We compare real-world and simulation-based training environments to determine changes in brain activation patterns and task performance. Methods: A total of twenty-six surgical trainees (general and gynecologic surgery residents and specialists) participated in this study. Participants completed standardized laparoscopic and robotic surgical tasks at varying levels of complexity while their cognitive workload was measured using fNIRS. This study included both simulation-based training and real-world surgical environments. Hemodynamic responses in the prefrontal cortex (PFC), task completion times, and performance metrics were analyzed. Results: Laparoscopic surgery elicited higher activity changes in the prefrontal cortex, indicating increased cognitive demand compared with robotic surgery, particularly for complex tasks like knot tying. Task complexity significantly influenced mental load, with more intricate procedures eliciting greater neural activation. Real-world training resulted in higher cognitive engagement than simulation, emphasizing the gap between simulated and actual surgical performance. Conclusions: Cognitive workload was lower and significantly different during robotic surgery than during laparoscopy, potentially due to its ergonomic advantages and enhanced motor control. Simulation-based training effectively prepares surgeons, but the cognitive workload results indicate that it may not fully replicate real-world surgical environments. These findings reveal the importance of cognitive workload assessment in surgical education and suggest that incorporating neuroimaging techniques such as fNIRS into training programs could enhance skill acquisition and performance.

Functional Near-Infrared Spectroscopy is (fNIRS) a non-invasive neuroimaging technique that monitors cerebral hemodynamic responses by measuring near-infrared (NIR) light absorption caused by changes in oxygenated and deoxygenated hemoglobin concentrations. While fNIRS has been widely used in cognitive and clinical neuroscience, a key challenge persists: the lack of practical tools required for calibrating source-detector separation (SDS) to maximize sensitivity at depth (SAD) for monitoring specific cortical regions of interest to neuroscience and neuroimaging studies. This study presents DrSVision version 1.0, a standalone software developed to address this limitation. Monte Carlo (MC) simulations were performed using segmented magnetic resonance imaging (MRI) data from eight cadaveric heads to realistically model light attenuation across anatomical layers. SAD of 10–20 mm with SDS of 19–39 mm was computed. The dataset was used to train a Gaussian Process Regression (GPR)-based machine learning (ML) model that recommends optimal SDS for achieving maximal sensitivity at targeted depths. The software operates independently of any third-party platforms and provides users with region-specific calibration outputs tailored for experimental goals, supporting more precise application of fNIRS. Future developments aim to incorporate subject-specific calibration using anatomical data and broaden support for diverse and personalized experimental setups. DrSVision represents a step forward in fNIRS experimentation.

The performance of Functional Near‐Infrared Spectroscopy (fNIRS) devices critically depends on the probe design, which affects signal quality, spatial and depth resolution, and data reliability. A critical component of probe separation is source‐to‐detector separation, which is defined as the distance between the light source and the detector. Optimizing this separation is essential for improving the signal‐to‐noise ratio (SNR) and sensitivity at depth (SAD). Larger separations enhance depth resolution, facilitating more accurate assessments of brain activity. Conversely, excessive separation may reduce SNR due to the lower light intensity received by the detector. In this study, a performance indicator was created to optimize separation by integrating the SNR and SAD. A probe was constructed that featured one light source and four detectors mounted on a mechanism that allowed for adjustable separations. A phantom mimicking brain tissue was used. Signals were recorded from the probe positioned on the phantom at various separations, employing light sources emitting light at wavelengths of 730, 800, and 850 nm, and optical power levels of 19, 26, 32, 38, and 44 mW. The SNR values for each separation were computed from the recorded signals, whereas the SAD values were obtained from existing literature. The performance indicator was developed as a weighted sum of SNR and SAD, normalized between 0 and 1, with higher values indicating enhanced probe performance due to optimized separation. The indicator is expected to improve the reliability of fNIRS data; however, further research involving diverse populations is required to validate its practical application.

Magnetic hyperthermia is a promising cancer treatment technique that relies on Néel and Brownian relaxation mechanisms to heat superparamagnetic nanoparticles injected into tumor sites. Under low-frequency magnetic fields, nanoparticles generate localized heat, inducing controlled thermal damage to cancer cells. However, radio frequency coils used to generate alternating magnetic fields may suffer from excessive heating, leading to efficiency losses and unintended thermal effects on surrounding healthy tissues. This study proposes novel liquid cooling systems, leveraging the skin effect phenomenon, to improve thermal management and reduce coil size. Finite element method-based simulation studies evaluated coil electrical current and temperature distributions under varying applied frequencies, water flow rates, and cooling microchannel dimensions. A dataset of 300 simulation cases was generated to train a Gaussian Process Regression-based machine learning model. The performance index was also developed and modeled using Gaussian Process Regression, enabling rapid performance prediction without requiring extensive numerical studies. Sensitivity analysis and the ReliefF algorithm were applied for a thorough analysis. Simulation results indicate that the proposed novel liquid cooling system demonstrates higher performance compared to conventional systems that utilize direct liquid cooling, offering a computationally efficient method for pre-manufacturing design optimization of radio frequency coil cooling systems in magnetic hyperthermia applications.