Assessment of Surgical Tasks Using Neuroimaging Dataset (ASTaUND).

Functional near-infrared spectroscopy (fNIRS) is a neuroimaging tool for studying brain activity in mobile subjects. Open-access fNIRS datasets are limited to simple and/or motion-restricted tasks. Here, we report a fNIRS dataset acquired on mobile subjects performing Fundamentals of Laparoscopic Surgery (FLS) tasks in a laboratory environment. Demonstrating competency in the FLS tasks is a prerequisite for board certification in general surgery in the United States. The ASTaUND data set was acquired over four different studies. We provide the relevant information about the hardware, FLS task execution protocols, and subject demographics to facilitate the use of this open-access data set. We also provide the concurrent FLS scores, a quantitative metric for surgical skill assessment developed by the FLS committee. This data set is expected to support the growing field of assessing surgical skills via neuroimaging data and provide an example of data processing pipeline for use in realistic, non-restrictive environments.

An ECG-based machine learning model for predicting new-onset atrial fibrillation is superior to age and clinical features in identifying patients at high stroke risk.

BACKGROUND: Several large trials have employed age or clinical features to select patients for atrial fibrillation (AF) screening to reduce strokes. We hypothesized that a machine learning (ML) model trained to predict AF risk from 12­lead electrocardiogram (ECG) would be more efficient than criteria based on clinical variables in indicating a population for AF screening to potentially prevent AF-related stroke. METHODS: We retrospectively included all patients with clinical encounters in Geisinger without a prior history of AF. Incidence of AF within 1 year and AF-related strokes within 3 years of the encounter were identified. AF-related stroke was defined as a stroke where AF was diagnosed at the time of stroke or within a year after the stroke. The efficiency of five methods was evaluated for selecting a cohort for AF screening. The methods were selected from four clinical trials (mSToPS, GUARD-AF, SCREEN-AF and STROKESTOP) and the ECG-based ML model. We simulated patient selection for the five methods between the years 2011 and 2014 and evaluated outcomes for 1 year intervals between 2012 and 2015, resulting in a total of twenty 1-year periods. Patients were considered eligible if they met the criteria before the start of the given 1-year period or within that period. The primary outcomes were numbers needed to screen (NNS) for AF and AF-associated stroke. RESULTS: The clinical trial models indicated large proportions of the population with a prior ECG for AF screening (up to 31%), coinciding with NNS ranging from 14 to 18 for AF and 249-359 for AF-associated stroke. At comparable sensitivity, the ECG ML model indicated a modest number of patients for screening (14%) and had the highest efficiency in NNS for AF (7.3; up to 60% reduction) and AF-associated stroke (223; up to 38% reduction). CONCLUSIONS: An ECG-based ML risk prediction model is more efficient than contemporary AF-screening criteria based on age alone or age and clinical features at indicating a population for AF screening to potentially prevent AF-related strokes.

Erratum: Functional brain connectivity related to surgical skill dexterity in physical and virtual simulation environments (Erratum).

[This corrects the article DOI: 10.1117/1.NPh.8.1.015008.].

Decreasing the Surgical Errors by Neurostimulation of Primary Motor Cortex and the Associated Brain Activation via Neuroimaging.

Acquisition of fine motor skills is a time-consuming process as it is based on learning via frequent repetitions. Transcranial electrical stimulation (tES) is a promising means of enhancing simple motor skill development via neuromodulatory mechanisms. Here, we report that non-invasive neurostimulation facilitates the learning of complex fine bimanual motor skills associated with a surgical task. During the training of 12 medical students on the Fundamentals of Laparoscopic Surgery (FLS) pattern cutting task over a period of 12 days, we observed that transcranial direct current stimulation (tDCS) decreased error level and the variability in performance, compared to the Sham group. Furthermore, by concurrently monitoring the cortical activations of the subjects via functional near-infrared spectroscopy (fNIRS), our study showed that the cortical activation patterns were significantly different between the tDCS and Sham group, with the activation of primary motor cortex (M1) and prefrontal cortex (PFC) contralateral to the anodal electrode significantly decreased while supplemental motor area (SMA) increased by tDCS. The lowered performance errors were retained after 1-month post-training. This work supports the use of tDCS to enhance performance accuracy in fine bimanual motor tasks.

Functional brain connectivity related to surgical skill dexterity in physical and virtual simulation environments.

Significance: Surgical simulators, both virtual and physical, are increasingly used as training tools for teaching and assessing surgical technical skills. However, the metrics used for assessment in these simulation environments are often subjective and inconsistent. Aim: We propose functional activation metrics, derived from brain imaging measurements, to objectively assess the correspondence between brain activation with surgical motor skills for subjects with varying degrees of surgical skill. Approach: Cortical activation based on changes in the oxygenated hemoglobin (HbO) of 36 subjects was measured using functional near-infrared spectroscopy at the prefrontal cortex (PFC), primary motor cortex, and supplementary motor area (SMA) due to their association with motor skill learning. Inter-regional functional connectivity metrics, namely, wavelet coherence (WCO) and wavelet phase coherence were derived from HbO changes to correlate brain activity to surgical motor skill levels objectively. Results: One-way multivariate analysis of variance found a statistically significant difference in the inter-regional WCO metrics for physical simulator based on Wilk's Λ for expert versus novice, F ( 10,1 ) = 7495.5 , p < 0.01 . Partial eta squared effect size for the inter-regional WCO metrics was found to be highest between the central prefrontal cortex (CPFC) and SMA, CPFC-SMA ( η 2 = 0.257 ). Two-tailed Mann-Whitney U tests with a 95% confidence interval showed baseline equivalence and a statistically significant ( p < 0.001 ) difference in the CPFC-SMA WPCO metrics for the physical simulator training group ( 0.960 ± 0.045 ) versus the untrained control group ( 0.735 ± 0.177 ) following training for 10 consecutive days in addition to the pretest and posttest days. Conclusion: We show that brain functional connectivity WCO metric corresponds to surgical motor skills in the laparoscopic physical simulators. Functional connectivity between the CPFC and the SMA is lower for subjects that exhibit expert surgical motor skills than untrained subjects in laparoscopic physical simulators.

Deep Neural Networks Can Predict New-Onset Atrial Fibrillation From the 12-Lead ECG and Help Identify Those at Risk of Atrial Fibrillation-Related Stroke.

BACKGROUND: Atrial fibrillation (AF) is associated with substantial morbidity, especially when it goes undetected. If new-onset AF could be predicted, targeted screening could be used to find it early. We hypothesized that a deep neural network could predict new-onset AF from the resting 12-lead ECG and that this prediction may help identify those at risk of AF-related stroke. METHODS: We used 1.6 M resting 12-lead digital ECG traces from 430 000 patients collected from 1984 to 2019. Deep neural networks were trained to predict new-onset AF (within 1 year) in patients without a history of AF. Performance was evaluated using areas under the receiver operating characteristic curve and precision-recall curve. We performed an incidence-free survival analysis for a period of 30 years following the ECG stratified by model predictions. To simulate real-world deployment, we trained a separate model using all ECGs before 2010 and evaluated model performance on a test set of ECGs from 2010 through 2014 that were linked to our stroke registry. We identified the patients at risk for AF-related stroke among those predicted to be high risk for AF by the model at different prediction thresholds. RESULTS: The area under the receiver operating characteristic curve and area under the precision-recall curve were 0.85 and 0.22, respectively, for predicting new-onset AF within 1 year of an ECG. The hazard ratio for the predicted high- versus low-risk groups over a 30-year span was 7.2 (95% CI, 6.9-7.6). In a simulated deployment scenario, the model predicted new-onset AF at 1 year with a sensitivity of 69% and specificity of 81%. The number needed to screen to find 1 new case of AF was 9. This model predicted patients at high risk for new-onset AF in 62% of all patients who experienced an AF-related stroke within 3 years of the index ECG. CONCLUSIONS: Deep learning can predict new-onset AF from the 12-lead ECG in patients with no previous history of AF. This prediction may help identify patients at risk for AF-related strokes.

Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network.

The electrocardiogram (ECG) is a widely used medical test, consisting of voltage versus time traces collected from surface recordings over the heart1. Here we hypothesized that a deep neural network (DNN) can predict an important future clinical event, 1-year all-cause mortality, from ECG voltage-time traces. By using ECGs collected over a 34-year period in a large regional health system, we trained a DNN with 1,169,662 12-lead resting ECGs obtained from 253,397 patients, in which 99,371 events occurred. The model achieved an area under the curve (AUC) of 0.88 on a held-out test set of 168,914 patients, in which 14,207 events occurred. Even within the large subset of patients (n = 45,285) with ECGs interpreted as 'normal' by a physician, the performance of the model in predicting 1-year mortality remained high (AUC = 0.85). A blinded survey of cardiologists demonstrated that many of the discriminating features of these normal ECGs were not apparent to expert reviewers. Finally, a Cox proportional-hazard model revealed a hazard ratio of 9.5 (P < 0.005) for the two predicted groups (dead versus alive 1 year after ECG) over a 25-year follow-up period. These results show that deep learning can add substantial prognostic information to the interpretation of 12-lead resting ECGs, even in cases that are interpreted as normal by physicians.

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

BACKGROUND: Physical 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 DESIGN: 18 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. RESULTS: Classification 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. CONCLUSION: fNIRS 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.

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.

Convergent validation and transfer of learning studies of a virtual reality-based pattern cutting simulator.

INTRODUCTION: Research has clearly shown the benefits of surgical simulators to train laparoscopic motor skills required for positive patient outcomes. We have developed the Virtual Basic Laparoscopic Skill Trainer (VBLaST) that simulates tasks from the Fundamentals of Laparoscopic Surgery (FLS) curriculum. This study aims to show convergent validity of the VBLaST pattern cutting module via the CUSUM method to quantify learning curves along with motor skill transfer from simulation environments to ex vivo tissue samples. METHODS: 18 medical students at the University at Buffalo, with no prior laparoscopic surgical skills, were placed into the control, FLS training, or VBLaST training groups. Each training group performed pattern cutting trials for 12 consecutive days on their respective simulation trainers. Following a 2-week break period, the trained students performed three pattern cutting trials on each simulation platform to measure skill retention. All subjects then performed one pattern cutting task on ex vivo cadaveric peritoneal tissue. FLS and VBLaST pattern cutting scores, CUSUM scores, and transfer task completion times were reported. RESULTS: Results indicate that the FLS and VBLaST trained groups have significantly higher task performance scores than the control group in both the VBLaST and FLS environments (p < 0.05). Learning curve results indicate that three out of seven FLS training subjects and four out of six VBLaST training subjects achieved the "senior" performance level. Furthermore, both the FLS and VBLaST trained groups had significantly lower transfer task completion times on ex vivo peritoneal tissue models (p < 0.05). CONCLUSION: We characterized task performance scores for trained VBLaST and FLS subjects via CUSUM analysis of the learning curves and showed evidence that both groups have significant improvements in surgical motor skill. Furthermore, we showed that learned surgical skills in the FLS and VBLaST environments transfer not only to the different simulation environments, but also to ex vivo tissue models.

Face and content validation of a Virtual Translumenal Endoscopic Surgery Trainer (VTEST™).

BACKGROUND: Natural orifice translumenal endoscopic surgery (NOTES) is an emerging surgical paradigm, where peritoneal access is achieved through one of the natural orifices of the body. It is being reported as a safe and feasible surgical technique with significantly reduced external scarring. Virtual Translumenal Endoscopic Surgical Trainer (VTEST™) is the first virtual reality simulator for the NOTES. The VTEST™ simulator was developed to train surgeons in the hybrid transvaginal NOTES cholecystectomy procedure. The initial version of the VTEST™ simulator underwent face validation at the 2013 Natural Orifice Surgery Consortium for Assessment and Research (NOSCAR) summit. Several areas of improvement were identified as a result, and the corresponding modifications were implemented in the simulator. This manuscript outlines the results of the subsequent evaluation study, performed in order to assess the face and content validity of the latest VTEST™ simulator. METHODS: Twelve subjects participated in an institutional review board-approved study that took place at the 2014 NOSCAR summit. Six of the 12 subjects, who are experts with NOTES experience, were used for face and content validation. The subjects performed the hybrid transvaginal NOTES cholecystectomy procedure on VTEST™ that included identifying the Calot's triangle, clipping and cutting the cystic duct/artery, and detaching the gallbladder. The subjects then answered five-point Likert scale feedback questionnaires for face and content validity. RESULTS: Overall, subjects rated 12/15 questions as 3.0 or greater (60 %), for face validity questions regarding the realism of the anatomical features, interface, and the tasks. Subjects also highly rated the usefulness of the simulator in learning the fundamental NOTES technical skills (3.50 ± 0.84). Content validity results indicate a high level of usefulness of the VTEST™ for training prior to operating room experience (4.17 ± 0.75).

Objective Surgical Skill Differentiation for Physical and Virtual Surgical Trainers via Functional Near-Infrared Spectroscopy.

This study proposes a methodology to objectively differentiate surgical skill for physical and virtual trainers by measuring functional activation between expert and novice surgeons. Results indicate that there is a significant increase in functional activation for novices in the right lateral prefrontal cortex, and decrease in the left medial primary motor cortex, and the supplementary motor area for the physical trainer (p<0.05). Results also indicate that there is a significant lower functional activation for novices compared to experts in the left medial primary motor cortex for the virtual skills trainer (p<0.05).

Monte carlo based simulation for evaluating optode fiber placement in prefrontal cortex imaging of motor skills during surgical training.

This study investigates the sensitivity of fiber placement positioning for the purpose of prefrontal cortex imaging using functional near-infrared spectroscopy (fNIRS). Results indicate that our proposed optode placement has higher scaled-absorption sensitivity than the traditional International 10-20 system for optode placement based on Monte Carlo simulations.

A comparison of NOTES transvaginal and laparoscopic cholecystectomy procedures based upon task analysis.

BACKGROUND: A virtual reality-based simulator for natural orifice translumenal endoscopic surgery (NOTES) procedures may be used for training and discovery of new tools and procedures. Our previous study (Sankaranarayanan et al. in Surg Endosc 27:1607-1616, 2013) shows that developing such a simulator for the transvaginal cholecystectomy procedure using a rigid endoscope will have the most impact on the field. However, prior to developing such a simulator, a thorough task analysis is necessary to determine the most important phases, tasks, and subtasks of this procedure. METHODS: 19 rigid endoscope transvaginal hybrid NOTES cholecystectomy procedures and 11 traditional laparoscopic procedures have been recorded and de-identified prior to analysis. Hierarchical task analysis was conducted for the rigid endoscope transvaginal NOTES cholecystectomy. A time series analysis was conducted to evaluate the performance of the transvaginal NOTES and laparoscopic cholecystectomy procedures. Finally, a comparison of electrosurgery-based errors was performed by two independent qualified personnel. RESULTS: The most time-consuming tasks for both laparoscopic and NOTES cholecystectomy are removing areolar and connective tissue surrounding the gallbladder, exposing Calot's triangle, and dissecting the gallbladder off the liver bed with electrosurgery. There is a positive correlation of performance time between the removal of areolar and connective tissue and electrosurgery dissection tasks in NOTES (r = 0.415) and laparoscopic cholecystectomy (r = 0.684) with p < 0.10. During the electrosurgery task, the NOTES procedures had fewer errors related to lack of progress in gallbladder removal. Contrarily, laparoscopic procedures had fewer errors due to the instrument being out of the camera view. CONCLUSION: A thorough task analysis and video-based quantification of NOTES cholecystectomy has identified the most time-consuming tasks. A comparison of the surgical errors during electrosurgery gallbladder dissection establishes that the NOTES procedure, while still new, is not inferior to the established laparoscopic procedure.

Resonance in the mouse tibia as a predictor of frequencies and locations of loading-induced bone formation.

To enhance new bone formation for the treating of patients with osteopenia and osteoporosis, various mechanical loading regimens have been developed. Although a wide spectrum of loading frequencies is proposed in those regimens, a potential linkage between loading frequencies and locations of loading-induced bone formation is not well understood. In this study, we addressed a question: Does mechanical resonance play a role in frequency-dependent bone formation? If so, can the locations of enhanced bone formation be predicted through the modes of vibration? Our hypothesis is that mechanical loads applied at a frequency near the resonant frequencies enhance bone formation, specifically in areas that experience high principal strains. To test the hypothesis, we conducted axial tibia loading using low, medium, or high frequency to the mouse tibia, as well as finite element analysis. The experimental data demonstrated dependence of the maximum bone formation on location and frequency of loading. Samples loaded with the low-frequency waveform exhibited peak enhancement of bone formation in the proximal tibia, while the high-frequency waveform offered the greatest enhancement in the midshaft and distal sections. Furthermore, the observed dependence on loading frequencies was correlated to the principal strains in the first five resonance modes at 8.0-42.9 Hz. Collectively, the results suggest that resonance is a contributor to the frequencies and locations of maximum bone formation. Further investigation of the observed effects of resonance may lead to the prescribing of personalized mechanical loading treatments.

The development of a haptic interface for the Virtual Translumenal Endoscopic Surgical Trainer (VTEST.

Natural orifice translumenal endoscopic surgery (NOTES) is an experimental surgical technique with benefits including reduced pain, post operative recovery period and better cosmesis compared to traditional laparoscopic procedures. In a pure NOTES procedure, a flexible endoscope is used for performing the surgery and visualization. The Virtual Translumenal Endoscopic Surgical Trainer (VTEST(TM)) is being developed as a platform to train for NOTES procedures and innovate NOTES tools and techniques. In this work we report the design specification for the hardware interface to be used for VTEST(TM).

Hierarchical task analysis of hybrid rigid scope Natural Orifice Translumenal Endoscopic Surgery (NOTES) cholecystectomy procedures.

This study shows task analysis results for the suturing task in the hybrid rigid scope Natural Orifice Translumenal Endoscopic Surgery (NOTES) cholecystectomy procedure. A hierarchical task analysis tree was constructed from the video recordings of the NOTES procedure and time analysis for the suturing subtask was performed. Results indicate that the "Pull Suture Through" subtask requires the greatest time (25.4 sec) and the "Re-bite" subtask had the highest variation (6.6 sec). Intra-rater reliability test (k = 0.68) also showed consistency of the results obtained from the video motion analysis.

Needs analysis for developing a virtual-reality NOTES simulator.

UNLABELLED: INTRODUCTION AND STUDY AIM: Natural orifice translumenal endoscopic surgery (NOTES) is an emerging surgical technique that requires a cautious adoption approach to ensure patient safety. High-fidelity virtual-reality-based simulators allow development of new surgical procedures and tools and train medical personnel without risk to human patients. As part of a project funded by the National Institutes of Health, we are developing the virtual transluminal endoscopic surgery trainer (VTEST) for this purpose. The objective of this study is to conduct a structured needs analysis to identify the design parameters for such a virtual-reality-based simulator for NOTES. METHODS: A 30-point questionnaire was distributed at the 2011 National Orifice Surgery Consortium for Assessment and Research meeting to obtain responses from experts. Ordinal logistic regression and the Wilcoxon rank-sum test were used for analysis. RESULTS: A total of 22 NOTES experts participated in the study. Cholecystectomy (CE, 68 %) followed by appendectomy (AE, 63 %) (CE vs AE, p = 0.0521) was selected as the first choice for simulation. Flexible (FL, 47 %) and hybrid (HY, 47 %) approaches were equally favorable compared with rigid (RI, 6 %) with p < 0.001 for both FL versus RI and HY versus RI. The transvaginal approach was preferred 3 to 1 to the transgastric. Most participants preferred two-channel (2C) scopes (65 %) compared with single (1C) or three (3C) or more channels with p < 0.001 for both 2C versus 1C and 2C versus 3C. The importance of force feedback and the utility of a virtual NOTES simulator in training and testing new tools for NOTES were rated very high by the participants. CONCLUSION: Our study reinforces the importance of developing a virtual NOTES simulator and clearly presents expert preferences. The results of this analysis will direct our initial development of the VTEST platform.

Automated real time peg and tool detection for the FLS trainer box.

This study proposes a method that effectively tracks trocar tool and peg positions in real time to allow real time assessment of the peg transfer task of the Fundamentals of Laparoscopic Surgery (FLS). By utilizing custom code along with OpenCV libraries, tool and peg positions can be accurately tracked without altering the original setup conditions of the FLS trainer box. This is achieved via a series of image filtration sequences, thresholding functions, and Haar training methods.