Open colorectal anastomosis remains difficult to support in virtual reality due to the need for unconstrained bimanual interaction and realistic soft-tissue behavior. We present the Virtual Colorectal Surgical Trainer (VCoST), a unified physics-based VR framework supporting both end-to-end hand-sewn anastomosis (VCoST-ETEHSA) and linear stapled anastomosis (VCoST-LSA). The system integrates deformable bowel models, phase-based procedural control, tracked instruments, and continuous performance logging. We evaluated VCoST-LSA with 24 participants (12 experts, 12 novices). Total rubric scores did not differ significantly between groups, though experts received higher scores, completed the task faster, and showed improved early bowel stabilization. Workload ratings indicated generally low-to-moderate perceived workload across subscales. While univariate kinematic metrics were not significant, multivariate analysis suggested dominant variability related to motion smoothness. Taken together, these findings suggest that VCoST-LSA captures complementary process-level kinematic signals that complement aggregate rubric scores; however, this initial study does not establish construct validity and motivates larger, confirmatory evaluations.

Although effective teamwork and communication are critical to surgical safety, structured training for non-technical skills (NTS) remains limited compared with technical simulation. The ACS/APDS Phase III Team-Based Skills Curriculum calls for scalable tools that both teach and objectively assess these competencies during laparoscopic emergencies. We introduce the Virtual Operating Room Team Experience (VORTeX), a multi-user virtual reality (VR) platform that integrates immersive team simulation with large language model (LLM) analytics to train and evaluate communication, decision-making, teamwork, and leadership. Team dialogue is analyzed using structured prompts derived from the Non-Technical Skills for Surgeons (NOTSS) framework, enabling automated classification of behaviors and generation of directed interaction graphs that quantify communication structure and hierarchy. Two laparoscopic emergency scenarios, pneumothorax and intra-abdominal bleeding, were implemented to elicit realistic stress and collaboration. Twelve surgical professionals completed pilot sessions at the 2024 SAGES conference, rating VORTeX as intuitive, immersive, and valuable for developing teamwork and communication. The LLM consistently produced interpretable communication networks reflecting expected operative hierarchies, with surgeons as central integrators, nurses as initiators, and anesthesiologists as balanced intermediaries. By integrating immersive VR with LLM-driven behavioral analytics, VORTeX provides a scalable, privacy-compliant framework for objective assessment and automated, data-informed debriefing across distributed training environments.

Motor skill acquisition in fields like surgery, robotics, and sports involves learning complex task sequences through extensive training. Traditional performance metrics, like execution time and error rates, offer limited insight as they fail to capture the neural mechanisms underlying skill learning and retention. This study introduces directed functional connectivity (dFC), derived from electroencephalography (EEG), as a novel brain-based biomarker for assessing motor skill learning and retention. For the first time, dFC is applied as a biomarker to map the stages of the Fitts and Posner motor learning model, offering new insights into the neural mechanisms underlying skill acquisition and retention. Unlike traditional measures, it captures both the strength and direction of neural information flow, providing a comprehensive understanding of neural adaptations across different learning stages. The analysis demonstrates that dFC can effectively identify and track the progression through various stages of the Fitts and Posner model. Furthermore, its stability over a six-week washout period highlights its utility in monitoring long-term retention. No significant changes in dFC were observed in a control group, confirming that the observed neural adaptations were specific to training and not due to external factors. By offering a granular view of the learning process at the group and individual levels, dFC facilitates the development of personalized, targeted training protocols aimed at enhancing outcomes in fields where precision and long-term retention are critical, such as surgical education. These findings underscore the value of dFC as a robust biomarker that complements traditional performance metrics, providing a deeper understanding of motor skill learning and retention.

BACKGROUND:Experience level correlates with motor cortex and supplementary motor area activation during laparoscopy. Whether brain activation patterns correlate with cognitive surgical task expertise is unknown. We compared the functional neuroimaging responses during simulated operative dictation-a cognitive surgical task-by experience level. STUDY DESIGN:Junior (postgraduate years 1-3) and senior (postgraduate years 4-5) residents and attendings were recruited over 1 year. After a baseline rest period, participants were asked to dictate a simulated operative note for an open inguinal hernia repair with mesh. Functional near-infrared spectroscopy data were recorded from the prefrontal, sensorimotor, and occipital brain areas. The hemodynamic response based on changes in oxyhemoglobin and deoxyhemoglobin concentrations during the task relative to the pre-task baseline for each participant were calculated. Group-level differences in oxyhemoglobin were evaluated using a general linear model. RESULTS:Thirty participants, 10 from each of the 3 experience levels, were recruited. In the left prefrontal cortex, senior activation (-182) was stronger than both junior (14) and attending (27) activation (P < .001). In the left premotor cortex, senior activation (-147) was stronger than both junior (-52) and attending (15) activation (P = .008). In the left parietal cortex, senior activation (-255) was stronger than both junior (-41) and attending (12) activation (P < .001). CONCLUSION:Functional neuroimaging responses during the cognitive task of simulated operative dictation differ by skill level. This study represents the first brain imaging analysis of cognitive function connecting mental imagery, brain activation, and a cognitive surgical task linked to previously performed motor tasks. Functional neuroimaging may act as a nonbiased assessment tool of cognitive skill.

In this work, we present a real-time virtual reality-based open surgery simulator that enables realistic soft-tissue suturing with bimanual haptic feedback. Our system uses eXtended Position-Based Dynamics (XPBD) for soft body and suture thread simulation, allowing stable real-time physics for complex interactions like continuous sutures and knot tying. In tests with all four common suturing techniques, purse-string, Connell, stay, and Lembert, the simulator maintained high frame rates (50-80 FPS) with up to 4,155 simulated particles, demonstrating consistent real-time performance. As part of our work, we conducted a user study using our suturing simulator, where 24 surgical trainees and experts used the Virtual Colorectal Surgery Trainer - Rectal Prolapse simulator. The user study showed that 71% of participants (n=17) rated the anatomical realism as moderate to very high. Half (n=12) found the force feedback realistic, and 54% (n=13) participants found the force feedback useful, indicating effective immersion while also highlighting the need for improved haptic fidelity. Overall, the simulation provides a low-cost, high-fidelity training platform for open surgical suturing, addressing a critical gap in current virtual reality educational tools.

Objective motor skill assessment plays a critical role in fields such as surgery, where proficiency is vital for certification and patient safety. Existing assessment methods, however, rely heavily on subjective human judgment, which introduces bias and limits reproducibility. While recent efforts have leveraged kinematic data and neural imaging to provide more objective evaluations, these approaches often overlook the dynamic neural mechanisms that differentiate expert and novice performance. This study proposes a novel method for motor skill assessment based on dynamic directed functional connectivity (dFC) as a neural biomarker. By using electroencephalography (EEG) to capture brain dynamics and employing an attention-based Long Short-Term Memory (LSTM) model for non-linear Granger causality analysis, we compute dFC among key brain regions involved in psychomotor tasks. Coupled with hierarchical task analysis (HTA), our approach enables subtask-level evaluation of motor skills, offering detailed insights into neural coordination that underpins expert proficiency. A convolutional neural network (CNN) is then used to classify skill levels, achieving greater accuracy and specificity than established performance metrics in laparoscopic surgery. This methodology provides a reliable, objective framework for assessing motor skills, contributing to the development of tailored training protocols and enhancing the certification process.

BACKGROUND:Endotracheal intubation (ETI) is an emergency procedure performed in civilians and combat casualty care settings to establish an airway. It's crucial that healthcare personnel are proficient in these skills, which traditionally have been evaluated through direct feedback from experts. Unfortunately, this method can be inconsistent and subjective, requiring considerable time and resources. METHODS:This study introduces a system for assessing ETI skills using video analysis. The system employs advanced video processing techniques, including a 2D convolutional autoencoder (AE) based on a self-supervision model, capable of recognizing complex patterns in videos. A 1D convolutional model enhanced with a cross-view attention module then uses AE features to make assessments. Data for the study was gathered in two phases, focusing first on comparisons between experts and novices, and then examining how novices perform under time constraints with outcomes labeled as either successful or unsuccessful. A separate set of data using videos from head-mounted cameras was also analyzed. RESULTS:The system successfully distinguishes between experts and novices in initial trials and demonstrates high accuracy in further classifications, including under time pressure and using head-mounted camera footage. CONCLUSIONS:This system's ability to accurately differentiate between experts and novices instills confidence in its effectiveness and potential to improve training and certification processes for healthcare providers.

The real-time assessment of complex motor skills presents a challenge in fields such as surgical training and rehabilitation. Recent advancements in neuroimaging, particularly functional near-infrared spectroscopy (fNIRS), have enabled objective assessment of such skills with high accuracy. However, these techniques are hindered by extensive preprocessing requirements to extract neural biomarkers. This study presents a novel end-to-end deep learning framework that processes raw fNIRS signals directly, eliminating the need for intermediate preprocessing steps. The model was evaluated on datasets from three distinct bimanual motor tasks–suturing, pattern cutting, and endotracheal intubation (ETI)–using performance metrics derived from both training and retention datasets. It achieved a mean classification accuracy of 93.9 retention datasets, with a leave-one-subject-out cross-validation yielding an accuracy of 94.1 exhibited task-specific discriminative power, while motor cortex activations consistently contributed to accurate classification. The model also demonstrated resilience to neurovascular coupling saturation caused by extended task sessions, maintaining robust performance across trials. Comparative analysis confirms that the end-to-end model performs on par with or surpasses baseline models optimized for fully processed fNIRS data, with statistically similar (p<0.05) or improved prediction accuracies. By eliminating the need for extensive signal preprocessing, this work provides a foundation for real-time, non-invasive assessment of bimanual motor skills in medical training environments, with potential applications in robotics, rehabilitation, and sports.

Cricothyroidotomy (CCT) is a critical, life-saving procedure requiring the identification of key neck landmarks through palpation. Interactive virtual simulation offers a promising, cost-effective approach to CCT training with high visual realism. However, developing the palpation skills necessary for CCT requires a haptic interface with tactile sensitivity comparable to human fingers. Such interfaces are often represented by plastic partial mannequins, which require further adaptation to integrate into virtual environments. This study introduces an instrumented physical palpation interface for CCT, integrated into a virtual surgical simulator, and tested on 10 surgeons who practiced the procedure over a training period. Data on haptic interactions collected during the training was analyzed to evaluate participants’ palpation skills and explore their force modulation strategies about landmark identification scores. Our findings suggest that trainees become more precise in their exploration over time, apply greater normal forces around target areas. Initial landmark identification performance influences adjustments in the overall applied pressure.

Ileal pouch-anal anastomosis (IPAA) is a key procedure to master in colorectal surgery. It is one task of the Colorectal Objective Structured Assessment of Technical Skill (COSATS) of the American Board of Colon and Rectal Surgery. The virtual colorectal surgical trainer-IPAA (VCoST-IPAA) was designed as an innovative platform for training and assessing performance in this procedure. Our aim was to establish the validity of the simulator by demonstrating its ability to distinguish between levels of surgical expertise. In this IRB-approved study, general surgery residents and colorectal surgeons from our institution performed the IPAA procedure on the VCoST simulator. Nineteen task-specific metrics developed by expert consensus were included and automatically recorded by the simulator. Participants were divided into novice (PGY 1–2) and experienced (PGY 3–5 and faculty) groups. The Messick’s unitary framework was used to assess the validity. The Mann–Whitney U test was used to compare the performance between the groups. A total of 22 equally distributed participants were included in this study. The Mann–Whitney U test showed significant differences in performance between the two groups on the assessment of J-pouch length (3.67 for experienced vs. 1.50 for novices; p = 0.01) and the gap indicator during trocar retraction (3.89 vs. 1.50; p = 0.04). No significant differences in completion time (670.1 vs. 826.3 s; W = 24; p = 0.09) nor the total score computed using 18 metrics (76.33 vs. 70.50; W = 65; p = 0.1) were found. Our VCoST-IPAA simulator showed that J-pouch length and the gap indicator during trocar retraction were important predictors of performance between experienced and novice participants. Participants in our study performed an Altemeier procedure on our validated VCoST-rectal prolapse simulator before the IPAA procedure, which may have had a positive effect on the performance on the VCoST-IPAA simulator.

Objective evaluation and feedback are crucial for effective simulation-based training in emergency medicine. However, the current evaluation paradigm is highly subjective, consequently suffers from poor inter-rater reliability. Feedback is entirely based on the discrepancy between a trainee's performance and the instructor's mental model of the procedure. This work directly addresses these limitations by developing an ensemble approach for the formative assessment of emergency medicine skills. The approach leverages a multimodal dataset comprising brain imaging, eye tracking, and head-mounted video recordings-each capturing distinct aspects of the brain-behavior relationship. While videos effectively capture behavioral elements, the brain activation signals and pupillometry provide physiological biomarkers that can be correlated with the stages of learning. The multimodal data are processed using a suite of explainable deep learning models to distill out biomarkers or behavioral measures that are shown to correlate with the endotracheal intubation (ETI) expertise level. Although all 3 modalities are shown to differentiate between successful and unsuccessful ETI tasks with greater than 85% accuracy, the brain activation and pupil dilation reveal a much-pronounced difference with accuracy >90%. The models are not only accurate, but they also identify the task segments where novice learners may benefit from targeted interventions. These findings demonstrate the unique potential of an ensemble approach for the objective and formative assessment of emergency medicine skill level.While.

The fracture characteristics of rare-earth phosphate (LuPO4) and silicate (Lu2SiO5) environmental barrier coating (EBC) materials under molten calcium-magnesium aluminosilicate (CMAS) corrosion are analyzed. EBCs are crucial for protecting SiC-based ceramic matrix composite components in the hot section of gas turbine engines. Recently the rare-earth phosphates as EBC materials have shown better performance than third-generation rare-earth silicates under CMAS corrosion. However, the fracture of EBCs under CMAS corrosion during service remains a significant concern. This work investigates the fracture characteristics of LuPO4 and Lu2SiO5 using a combined experimental and computational approach. The computational model uses experimental micrographs and material properties obtained from fabricated EBC samples for fracture simulations. The simulation results are compared with experimental fracture toughness data and validated using statistical tests (p < 0.01). The results show significant degradation in fracture strength of EBC materials caused by CMAS penetration. EBC materials lost more than 40% of their initial fracture strength even at low penetration levels of 3% by volume. Simulation results show that LuPO4 degraded more than Lu2SiO5. However, experimental observations from CMAS reaction tests demonstrate that LuPO4 may exhibit higher fracture resistance than Lu2SiO5 under similar CMAS corrosion conditions due to the formation of dense and thick passivation reaction layer. The insights gained from this study could be used to design EBCs with improved fracture resistance under CMAS corrosion.

Virtual Bariatric Endoscopy (ViBE) simulator is designed for Endoscopic Sleeve Gastroplasty (ESG), a minimally invasive bariatric procedure. While virtual simulators exist for bariatric surgeries, there has been a lack of ESG-specific tools. The ViBE simulator fills this gap by providing a cost-effective alternative to physical models and enhancing ESG training. The simulator consists of three main components: software simulation, hardware, and a hardware interface linking the two. The software focuses on ESG techniques like marking, suturing, and tissue-pulling, with an algorithm for suturing and soft-body physics simulations. The simulator features two human-computer interfaces: one using USB-HID protocol with optical encoders and ARM Cortex M7 devices, and another using computer vision for delicate instruments. The computer vision interface simplifies mechanical design. Performance tests showed an average of 55 FPS, with render times between 2ms and 4ms and solver times between 16ms and 18ms. The end-to-end delay was under 75ms, and haptic feedback forces updated every 1ms. The ViBE simulator aims to improve ESG training and suturing techniques, demonstrating its potential as an effective learning tool with efficient software performance and minimal hardware latency.

Airway management skills are critical in emergency medicine and are typically assessed through subjective evaluation, often failing to gauge competency in real-world scenarios. This paper proposes a machine learning-based approach for assessing airway skills, specifically endotracheal intubation (ETI), using human gaze data and video recordings. The proposed system leverages an attention mechanism guided by the human gaze to enhance the recognition of successful and unsuccessful ETI procedures. Visual masks were created from gaze points to guide the model in focusing on task-relevant areas, reducing irrelevant features. An autoencoder network extracts features from the videos, while an attention module generates attention from the visual masks, and a classifier outputs a classification score. This method, the first to use human gaze for ETI, demonstrates improved accuracy and efficiency over traditional methods. The integration of human gaze data not only enhances model performance but also offers a robust, objective assessment tool for clinical skills, particularly in high-stress environments such as military settings. The results show improvements in prediction accuracy, sensitivity, and trustworthiness, highlighting the potential for this approach to improve clinical training and patient outcomes in emergency medicine.

INTRODUCTION:Endotracheal intubation (ETI) is a critical procedure that requires effective training and assessment to ensure successful oxygen delivery. Traditional training methods, such as observation and checklist-based assessments, are resource-intensive and heavily reliant on expert supervision. This study evaluates provider posture during ETI and explores the use of video-based posture analysis and machine learning to classify successful intubation attempts. MATERIALS AND METHODS:Eighteen novice participants performed ETI on a manikin for 30 repetitions over 3 visits (10 per visit) within 1 week. Videos of the third visit were cropped to the final 5 seconds of the task and processed at 4 frames per second. Provider posture points were extracted using a pose detection network, and relative distances between points were calculated as features across a dataset of 2,429 frames. After filtering out incomplete data, the 922-frame dataset was split for 10-fold cross-validation in an XGBoost model. RESULTS:The model achieved an average test performance of 77.8% accuracy, 76.9% sensitivity, and 78.7% specificity on average across folds in classifying successful and unsuccessful intubations. Feature analysis identified key spatial relationships, such as the relative positions of elbows and wrists, as significant predictors of success. DISCUSSION:This study demonstrates the potential of video-based posture analysis in objectively evaluating ETI performance. The approach minimizes the need for expert oversight and offers a scalable solution for training in resource-constrained settings. Although additional validation is needed, this method could enhance training effectiveness in environments such as military healthcare. CONCLUSION:Integrating posture analysis and machine learning provides a practical, scalable framework for assessing ETI performance. Future research should focus on refining this methodology and exploring its integration into current training programs.

This study presents a novel approach using graph neural networks to predict the risk of internal bleeding using vessel maps derived from patient CT and MRI scans, aimed at enhancing the realism of surgical simulators for emergency scenarios such as trauma, where rapid detection of internal bleeding can be lifesaving. First, medical images are segmented and converted into graph representations of the vasculature, where nodes represent vessel branching points with spatial coordinates and edges encode vessel features such as length and radius. Due to no existing dataset directly labeling bleeding risks, we calculate the bleeding probability for each vessel node using a physics-based heuristic, peripheral vascular resistance via the Hagen-Poiseuille equation. A graph attention network is then trained to regress these probabilities, effectively learning to predict hemorrhage risk from the graph-structured imaging data. The model is trained using a tenfold cross-validation on a combined dataset of 1708 vessel graphs extracted from four public image datasets (MSD, KiTS, AbdomenCT, CT-ORG) with optimization via the Adam optimizer, mean squared error loss, early stopping, and L2 regularization. Our model achieves a mean R-squared of 0.86, reaching up to 0.9188 in optimal configurations and low mean training and validation losses of 0.0069 and 0.0074, respectively, in predicting bleeding risk, with higher performance on well-connected vascular graphs. Finally, we integrate the trained model into an immersive virtual reality environment to simulate intra-abdominal bleeding scenarios for immersive surgical training. The model demonstrates robust predictive performance despite the inherent sparsity of real-life datasets.

A moment–curvature constitutive model is proposed for the dynamic simulation of visco-plastic rods subject to time-varying loads and constraints at interactive rates. Smooth spline functions are used to discretize the geometry of the rod and its kinematics with the centerline coordinates as degrees of freedom (DOF) and scalar twist as degrees of freedom (DOF). The plastic curvature is defined as a uniformly varying field in contrast to localized lumped plasticity models, suitable for simulation of spatial rods that undergo uniform plastic deformation such as a cable or surgical suture thread. The yield criterion and plastic/visco-plastic flow rule are developed for spatial rods taking advantage of the availability of smooth moment–curvature fields using the spline-based formulation. With the Bishop frame field as a reference, the material curvatures are quantified using the twist degree of freedom, enabling tracking the plastic fields with scalar twist, thereby eliminating slopes as DOF. Taking advantage of the invariant sub-blocks and the sparsity of the dynamic system matrix arising from the numerical discretization, an hierarchical (H-matrix) solution approach is utilized for efficient computation. Uniform curvature bending tests and moment relaxation tests are performed to study the convergence behavior of the model. Several real-world tests involving contact are performed to demonstrate the applicability of the model in interactive simulations.

Perineal proctectomy is a complex procedure that requires advanced skills. Currently, there are no simulators for training in this procedure. As part of our objective of developing a virtual reality simulator, our goal was to develop and validate task-specific metrics for the assessment of performance for this procedure. We conducted a three-phase study to establish task-specific metrics, obtain expert consensus on the appropriateness of the developed metrics, and establish the discriminant validity of the developed metrics. In phase I, we utilized hierarchical task analysis to formulate the metrics. In phase II, a survey involving expert colorectal surgeons determined the significance of the developed metrics. Phase III was aimed at establishing the discriminant validity for novices (PGY1-3) and experts (PGY4-5 and faculty). They performed a perineal proctectomy on a rectal prolapse model. Video recordings were independently assessed by two raters using global ratings and task-specific metrics for the procedure. Total scores for both metrics were computed and analyzed using the Kruskal–Wallis test. A Mann–Whitney U test with Benjamini–Hochberg correction was used to evaluate between-group differences. Spearman’s rank correlation coefficient was computed to assess the correlation between global and task-specific scores. In phase II, a total of 23 colorectal surgeons were recruited and consensus was obtained on all the task-specific metrics. In phase III, participants (n = 22) included novices (n = 15) and experts (n = 7). There was a strong positive correlation between the global and task-specific scores (rs = 0.86; P < 0.001). Significant between-group differences were detected for both global (χ2 = 15.38; P < 0.001; df = 2) and task-specific (χ2 = 11.38; P = 0.003; df = 2) scores. Using a biotissue rectal prolapse model, this study documented high IRR and significant discriminant validity evidence in support of video-based assessment using task-specific metrics.

Current training methods for surgical trainees are inadequate because they are costly, low-fidelity, or have a low skill ceiling. This work aims to expand available virtual reality training options by developing a VR trainer for straight coloanal anastomosis (SCA), one of the Colorectal Objective Structured Assessment of Technical Skills (COSATS) tasks. We developed a VR-based SCA simulator to evaluate trainees based on their performance. To increase the immersiveness, alongside the VR headset, we used haptics as the primary method of interaction with the simulation. We also implemented objective performance metrics to evaluate trainee performance throughout the simulation. We presented our performance metrics to 27 participants for an Expert Consensus Survey (5-point Likert scale) and created weights for our metrics. The weighted average scores for the 24 task-specific metrics ranged from 3.5 to 5. Additionally, for the general metrics, the scores spanned from 3.3 to 4.6. In the second phase of our study, we conducted a study with 16 participants (novice n = 9, expert n = 7). Based on the performance, experts outperformed novices by 8.56

Background and Aims:Obesity is a global health concern. Bariatric surgery offers reliably effective and durable weight loss and improvements of other comorbid conditions. However, the accessibility of bariatric surgery remains limited. Minimally invasive techniques, including endoscopic sleeve gastroplasty (ESG), have emerged to bridge this gap. To effectively complete the ESG procedure, one requires skill in multiple complex interventional endoscopic maneuvers. This requisite expertise poses challenges for training in this burgeoning field. Methods:We designed the virtual bariatric endoscopic (ViBE) simulator software to mimic the ESG procedure accurately. The ViBE simulator features a detailed simulation of an endoscope equipped with an endoscopic suturing system and a high-resolution stomach, enhancing the visualization of procedural details. Furthermore, the simulator incorporates performance metrics using a reverse scoring system to evaluate users' proficiency in tasks such as argon plasma coagulation (APC) marking, suturing, and cinching. To validate the simulator, we conducted a study involving experts and novices at the Indiana University School of Medicine, where participants engaged with the simulation environment in a series of training tasks. Results:Twelve participants, comprising 5 experts and 7 novices, were asked to complete a post-training questionnaire featuring 7 items, rating each on a Likert scale. The APC task realism received the highest score, averaging 3.83. The usefulness of improving endoscopic technical skills averaged 3.08, with the realism of cinching the knot and suturing tasks receiving scores of 3.17 and 3.25, respectively, suggesting a generally positive reception. Automated performance metrics indicated that, on average, experts outperformed novices by 10.83 points. Conclusions:The ViBE simulation strives to replicate the steps of the ESG within a virtual environment. Our primary objective in developing this simulator was to enhance the learning curve for endoscopic suturing and ESG techniques, thereby safely extending these skills to a broader patient base.