Disparity in Frontal Lobe Connectivity on a Complex Bimanual Motor Task Aids in Classification of Operator Skill Level

Using neuroimaging to assess brain activity and areas associated with surgical skills: a systematic review

BACKGROUND

Surgical skills acquisition is under continuous development due to the emergence of new technologies, and there is a need for assessment tools to develop along with these. A range of neuroimaging modalities has been used to map the functional activation of brain networks while surgeons acquire novel surgical skills. These have been proposed as a method to provide a deeper understanding of surgical expertise and offer new possibilities for the personalized training of future surgeons. With studies differing in modalities, outcomes, and surgical skills there is a need for a systematic review of the evidence. This systematic review aims to summarize the current knowledge on the topic and evaluate the potential use of neuroimaging in surgical education.

METHODS

We conducted a systematic review of neuroimaging studies that mapped functional brain activation while surgeons with different levels of expertise learned and performed technical and non-technical surgical tasks. We included all studies published before July 1st, 2023, in MEDLINE, EMBASE and WEB OF SCIENCE.

RESULTS

38 task-based brain mapping studies were identified, consisting of randomized controlled trials, case-control studies, and observational cohort or cross-sectional studies. The studies employed a wide range of brain mapping modalities, including electroencephalography, functional magnetic resonance imaging, positron emission tomography, and functional near-infrared spectroscopy, activating brain areas involved in the execution and sensorimotor or cognitive control of surgical skills, especially the prefrontal cortex, supplementary motor area, and primary motor area, showing significant changes between novices and experts.

CONCLUSION

Functional neuroimaging can reveal how task-related brain activity reflects technical and non-technical surgical skills. The existing body of work highlights the potential of neuroimaging to link task-related brain activity patterns with the individual level of competency or improvement in performance after training surgical skills. More research is needed to establish its validity and usefulness as an assessment tool.

Physical and biophysical markers of assessment in medical training: A scoping review of the literature

PURPOSE

Assessment in medical education has changed over time to measure the evolving skills required of current medical practice. Physical and biophysical markers of assessment attempt to use technology to gain insight into medical trainees' knowledge, skills, and attitudes. The authors conducted a scoping review to map the literature on the use of physical and biophysical markers of assessment in medical training.

MATERIALS AND METHODS

The authors searched seven databases on 1 August 2022, for publications that utilized physical or biophysical markers in the assessment of medical trainees (medical students, residents, fellows, and synonymous terms used in other countries). Physical or biophysical markers included: heart rate and heart rate variability, visual tracking and attention, pupillometry, hand motion analysis, skin conductivity, salivary cortisol, functional magnetic resonance imaging (fMRI), and functional near-infrared spectroscopy (fNIRS). The authors mapped the relevant literature using Bloom's taxonomy of knowledge, skills, and attitudes and extracted additional data including study design, study environment, and novice vs. expert differentiation from February to June 2023.

RESULTS

Of 6,069 unique articles, 443 met inclusion criteria. The majority of studies assessed trainees using heart rate variability (n = 160, 36%) followed by visual attention (n = 143, 32%), hand motion analysis (n = 67, 15%), salivary cortisol (n = 67, 15%), fMRI (n = 29, 7%), skin conductivity (n = 26, 6%), fNIRs (n = 19, 4%), and pupillometry (n = 16, 4%). The majority of studies (n = 167, 38%) analyzed non-technical skills, followed by studies that analyzed technical skills (n = 155, 35%), knowledge (n = 114, 26%), and attitudinal skills (n = 61, 14%). 169 studies (38%) attempted to use physical or biophysical markers to differentiate between novice and expert.

CONCLUSION

This review provides a comprehensive description of the current use of physical and biophysical markers in medical education training, including the current technology and skills assessed. Additionally, while physical and biophysical markers have the potential to augment current assessment in medical education, there remains significant gaps in research surrounding reliability, validity, cost, practicality, and educational impact of implementing these markers of assessment.

Neurophysiological, muscular, and perceptual adaptations of exoskeleton use over days during overhead work with competing cognitive demands

This study captured neurophysiological, muscular, and perceptual adaptations to shoulder exoskeleton use during overhead work with competing physical-cognitive demands. Twenty-four males and females, randomly divided into control and exoskeleton groups, performed an overhead reaching and pointing task over three days without (single task) and with (dual task) a working memory task. Task performance, electromyography (EMG), neural activity, heart rate, and subjective responses were collected. While task completion time reduced for both groups at the same rate over days, EMG activity of shoulder muscles was lower for the exoskeleton group for both tasks, specifically for females during the dual task. Dual task reduced the physiological benefits of exoskeletons and neuromotor strategies to adapt to the dual task demands differed between the groups. Neuromuscular benefits of exoskeleton use were immediately realized irrespective of cognitive demand, however the perceptual, physiological, and neural adaptations with exoskeleton use were task- and sex-specific.

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.

Optical neuroimaging and neurostimulation in surgical training and assessment: A state-of-the-art review

Introduction

Functional near-infrared spectroscopy (fNIRS) is a non-invasive optical neuroimaging technique used to assess surgeons' brain function. The aim of this narrative review is to outline the effect of expertise, stress, surgical technology, and neurostimulation on surgeons' neural activation patterns, and highlight key progress areas required in surgical neuroergonomics to modulate training and performance.

Methods

A literature search of PubMed and Embase was conducted to identify neuroimaging studies using fNIRS and neurostimulation in surgeons performing simulated tasks.

Results

Novice surgeons exhibit greater haemodynamic responses across the pre-frontal cortex than experts during simple surgical tasks, whilst expert surgical performance is characterized by relative prefrontal attenuation and upregulation of activation foci across other regions such as the supplementary motor area. The association between PFC activation and mental workload follows an inverted-U shaped curve, activation increasing then attenuating past a critical inflection point at which demands outstrip cognitive capacity Neuroimages are sensitive to the impact of laparoscopic and robotic tools on cognitive workload, helping inform the development of training programs which target neural learning curves. FNIRS differs in comparison to current tools to assess proficiency by depicting a cognitive state during surgery, enabling the development of cognitive benchmarks of expertise. Finally, neurostimulation using transcranial direct-current-stimulation may accelerate skill acquisition and enhance technical performance.

Conclusion

FNIRS can inform the development of surgical training programs which modulate stress responses, cognitive learning curves, and motor skill performance. Improved data processing with machine learning offers the possibility of live feedback regarding surgeons' cognitive states during operative procedures.

Use of neuroimaging to measure neurocognitive engagement in health professions education: a scoping review

PURPOSE

To map the current literature on functional neuroimaging use in medical education research as a novel measurement modality for neurocognitive engagement, learning, and expertise development.

METHOD

We searched PubMed, Embase, Cochrane, ERIC, and Web of Science, and hand-searched reference lists of relevant articles on April 4, 2019, and updated the search on July 7, 2020. Two authors screened the abstracts and then full-text articles for eligibility based on inclusion criteria. The data were then charted, synthesized, and analyzed descriptively.

RESULTS

Sixty-seven articles published between 2007 and 2020 were included in this scoping review. These studies used three main neuroimaging modalities: functional magnetic resonance imaging, functional near-infrared spectroscopy, and electroencephalography. Most of the publications (90%, n = 60) were from the last 10 years (2011-2020). Although these studies were conducted in 16 countries, 68.7% (n = 46) were from three countries: the USA (n = 21), UK (n = 15), and Canada (n = 10). These studies were mainly non-experimental (74.6%, n = 50). Most used neuroimaging techniques to examine psychomotor skill development (57%, n = 38), but several investigated neurocognitive correlates of clinical reasoning skills (22%, n = 15).

CONCLUSION

This scoping review maps the available literature on functional neuroimaging use in medical education. Despite the heterogeneity in research questions, study designs, and outcome measures, we identified a few common themes. Included studies are encouraging of the potential for neuroimaging to complement commonly used measures in education research and may help validate/challenge established theoretical assumptions and provide insight into training methods. This review highlighted several areas for further research. The use of these emerging technologies appears ripe for developing precision education, establishing viable study protocols for realistic operational settings, examining team dynamics, and exploring applications for real-time monitoring/intervention during critical clinical tasks.

The Measurement of Cognitive Workload in Surgery Using Pupil Metrics: A Systematic Review and Narrative Analysis

INTRODUCTION

Increased cognitive workload (CWL) is a well-established entity that can impair surgical performance and increase the likelihood of surgical error. The use of pupil and gaze tracking data is increasingly being used to measure CWL objectively in surgery. The aim of this review is to summarize and synthesize the existing evidence that surrounds this.

METHODS

A systematic review was undertaken in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A search of OVID MEDLINE, IEEE Xplore, Web of Science, Google Scholar, APA PsychINFO, and EMBASE was conducted for articles published in English between 1990 and January 2021. In total, 6791 articles were screened and 32 full-text articles were selected based on the inclusion criteria. A narrative analysis was undertaken in view of the heterogeneity of studies.

RESULTS

Seventy-eight percent of selected studies were deemed high quality. The most frequent surgical environment and task studied was surgical simulation (75%) and performance of laparoscopic skills (56%) respectively. The results demonstrated that the current literature can be broadly categorized into pupil, blink, and gaze metrics used in the assessment of CWL. These can be further categorized according to their use in the context of CWL: (1) direct measurement of CWL (n = 16), (2) determination of expertise level (n = 14), and (3) predictors of performance (n = 2).

CONCLUSIONS

Eye-tracking data provide a wealth of information; however, there is marked study heterogeneity. Pupil diameter and gaze entropy demonstrate promise in CWL assessment. Future work will entail the use of artificial intelligence in the form of deep learning and the use of a multisensor platform to accurately measure CWL.

Neuromonitoring Correlates of Expertise Level in Surgical Performers: A Systematic Review

Surgical expertise does not have a clear definition and is often culturally associated with power, authority, prestige, and case number rather than more objective proxies of excellence. Multiple models of expertise progression have been proposed including the Dreyfus model, however, they all currently require subjective evaluation of skill. Recently, efforts have been made to improve the ways in which surgical excellence is measured and expertise is defined using artificial intelligence, video recordings, and accelerometers. However, these aforementioned methods of assessment are still subjective or indirect proxies of expertise, thus uncovering the neural mechanisms that differentiate expert surgeons from trainees may enhance the objectivity of surgical expertise validation. In fact, some researchers have already suggested that their neural imaging-based expertise classification methods outperform currently used methods of surgical skill certification such as the Fundamentals of Laparoscopic Surgery (FLS) scores. Such imaging biomarkers would not only help better identify the highest performing surgeons, but could also improve residency programs by providing more objective, evidence-based feedback and developmental milestones for those in training and perhaps act as a marker of surgical potential in medical students. Despite the potential advantages of using neural imaging in the assessment of surgical expertise, this field of research remains in its infancy. This systematic review identifies studies that have applied neuromonitoring in assessing surgical skill across levels of expertise. The goals of this review are to identify (1) the strongest neural indicators of surgical expertise, (2) the limitations of the current literature on this subject, (3) the most sensible future directions for further study. We found substantial evidence that surgical expertise can be delineated by differential activation and connectivity in the prefrontal cortex (PFC) across multiple task and neuroimaging modalities. Specifically, novices tend to have greater PFC activation than experts under standard conditions in bimanual and decision-making tasks. However, under high temporal demand tasks, experts had increased PFC activation whereas novices had decreased PFC activation. Common limitations uncovered in this review were that task difficulty was often insufficient to delineate between residents and attending. Moreover, attending level involvement was also low in multiple studies which may also have contributed to this issue. Most studies did not analyze the ability of their neuromonitoring findings to accurately classify subjects by level of expertise. Finally, the predominance of fNIRS as the neuromonitoring modality limits our ability to uncover the neural correlates of surgical expertise in non-cortical brain regions. Future studies should first strive to address these limitations. In the longer term, longitudinal within-subjects design over the course of a residency or even a career will also advance the field. Although logistically arduous, such studies would likely be most beneficial in demonstrating effects of increasing surgical expertise on regional brain activation and inter-region connectivity.

Exploring the roles of artificial intelligence in surgical education: A scoping review

BACKGROUND

Technology-enhanced teaching and learning, including Artificial Intelligence (AI) applications, has started to evolve in surgical education. Hence, the purpose of this scoping review is to explore the current and future roles of AI in surgical education.

METHODS

Nine bibliographic databases were searched from January 2010 to January 2021. Full-text articles were included if they focused on AI in surgical education.

RESULTS

Out of 14,008 unique sources of evidence, 93 were included. Out of 93, 84 were conducted in the simulation setting, and 89 targeted technical skills. Fifty-six studies focused on skills assessment/classification, and 36 used multiple AI techniques. Also, increasing sample size, having balanced data, and using AI to provide feedback were major future directions mentioned by authors.

CONCLUSIONS

AI can help optimize the education of trainees and our results can help educators and researchers identify areas that need further investigation.

Characterization of a Raspberry Pi as the Core for a Low-cost Multimodal EEG-fNIRS Platform

Poor understanding of brain recovery after injury, sparsity of evaluations and limited availability of healthcare services hinders the success of neurorehabilitation programs in rural communities. The availability of neuroimaging ca-pacities in remote communities can alleviate this scenario supporting neurorehabilitation programs in remote settings. This research aims at building a multimodal EEG-fNIRS neuroimaging platform deployable to rural communities to support neurorehabilitation efforts. A Raspberry Pi 4 is chosen as the CPU for the platform responsible for presenting the neurorehabilitation stimuli, acquiring, processing and storing concurrent neuroimaging records as well as the proper synchronization between the neuroimaging streams. We present here two experiments to assess the feasibility and characterization of the Raspberry Pi as the core for a multimodal EEG-fNIRS neuroimaging platform; one over controlled conditions using a combination of synthetic and real data, and another from a full test during resting state. CPU usage, RAM usage and operation temperature were measured during the tests with mean operational records below 40% for CPU cores, 13.6% for memory and 58.85 ° C for temperatures. Package loss was inexistent on synthetic data and negligible on experimental data. Current consumption can be satisfied with a 1000 mAh 5V battery. The Raspberry Pi 4 was able to cope with the required workload in conditions of operation similar to those needed to support a neurorehabilitation evaluation.

Explainable artificial intelligence based analysis for interpreting infant fNIRS data in developmental cognitive neuroscience

In the last decades, non-invasive and portable neuroimaging techniques, such as functional near infrared spectroscopy (fNIRS), have allowed researchers to study the mechanisms underlying the functional cognitive development of the human brain, thus furthering the potential of Developmental Cognitive Neuroscience (DCN). However, the traditional paradigms used for the analysis of infant fNIRS data are still quite limited. Here, we introduce a multivariate pattern analysis for fNIRS data, xMVPA, that is powered by eXplainable Artificial Intelligence (XAI). The proposed approach is exemplified in a study that investigates visual and auditory processing in six-month-old infants. xMVPA not only identified patterns of cortical interactions, which confirmed the existent literature; in the form of conceptual linguistic representations, it also provided evidence for brain networks engaged in the processing of visual and auditory stimuli that were previously overlooked by other methods, while demonstrating similar statistical performance.

Single-Trial Recognition of Video Gamer's Expertise from Brain Haemodynamic and Facial Emotion Responses

With an increase in consumer demand of video gaming entertainment, the game industry is exploring novel ways of game interaction such as providing direct interfaces between the game and the gamers’ cognitive or affective responses. In this work, gamer’s brain activity has been imaged using functional near infrared spectroscopy (fNIRS) whilst they watch video of a video game (League of Legends) they play. A video of the face of the participants is also recorded for each of a total of 15 trials where a trial is defined as watching a gameplay video. From the data collected, i.e., gamer’s fNIRS data in combination with emotional state estimation from gamer’s facial expressions, the expertise level of the gamers has been decoded per trial in a multi-modal framework comprising of unsupervised deep feature learning and classification by state-of-the-art models. The best tri-class classification accuracy is obtained using a cascade of random convolutional kernel transform (ROCKET) feature extraction method and deep classifier at 91.44%. This is the first work that aims at decoding expertise level of gamers using non-restrictive and portable technologies for brain imaging, and emotional state recognition derived from gamers’ facial expressions. This work has profound implications for novel designs of future human interactions with video games and brain-controlled games.

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.

Use of prefrontal cortex activity as a measure of learning curve in surgical novices: results of a single blind randomised controlled trial

BACKGROUND

Neurobiological feedback in surgical training could translate to better educational outcomes such as measures of learning curve. This work examined the variation in brain activation of medical students when performing laparoscopic tasks before and after a training workshop, using functional near-infrared spectroscopy (fNIRS).

METHODS AND PROCEDURES

This single blind randomised controlled trial examined the prefrontal cortex activity (PFCA) differences in two groups of novice medical students during the acquisition of four laparoscopic tasks. Both groups were shown a basic tutorial video, with the "Trained-group" receiving an additional standardised one-to-one training on the tasks. The PFCA was measured pre- and post-intervention using a portable fNIRS device and reported as mean total oxygenated hemoglobin (HbOµm). Primary outcome of the study is the difference in HbOµm between post- and pre-intervention readings for each of the four laparoscopic tasks. The pre- and post-intervention laparoscopic tasks were recorded and assessed by two blinded individual assessors for objective scores of the performance.

RESULTS

16 Trained and 16 Untrained, right-handed medical students with an equal sex distribution and comparable age distribution were recruited. Trained group had an attenuated left PFCA in the "Precision cutting" (p = 0.007) task compared to the Untrained group. Subgroup analysis by sex revealed attenuation in left PFCA in Trained females compared to Untrained females across two laparoscopic tasks: "Peg transfer" (p = 0.005) and "Precision cutting" (p = 0.003). No significant PFCA attenuation was found in male students who underwent training compared to Untrained males.

CONCLUSION

A standardised laparoscopic training workshop promoted greater PFCA attenuation in female medical students compared to males. This suggests that female and male students respond differently to the same instructional approach. Implications include a greater focus on one-to-one surgical training for female students and use of PFCA attenuation as a form of neurobiological feedback in surgical training.

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.

Using Machine Learning to Assess Physician Competence: A Systematic Review

PURPOSE

To identify the different machine learning (ML) techniques that have been applied to automate physician competence assessment and evaluate how these techniques can be used to assess different competence domains in several medical specialties.

METHOD

In May 2017, MEDLINE, EMBASE, PsycINFO, Web of Science, ACM Digital Library, IEEE Xplore Digital Library, PROSPERO, and Cochrane Database of Systematic Reviews were searched for articles published from inception to April 30, 2017. Studies were included if they applied at least one ML technique to assess medical students', residents', fellows', or attending physicians' competence. Information on sample size, participants, study setting and design, medical specialty, ML techniques, competence domains, outcomes, and methodological quality was extracted. MERSQI was used to evaluate quality, and a qualitative narrative synthesis of the medical specialties, ML techniques, and competence domains was conducted.

RESULTS

Of 4,953 initial articles, 69 met inclusion criteria. General surgery (24; 34.8%) and radiology (15; 21.7%) were the most studied specialties; natural language processing (24; 34.8%), support vector machine (15; 21.7%), and hidden Markov models (14; 20.3%) were the ML techniques most often applied; and patient care (63; 91.3%) and medical knowledge (45; 65.2%) were the most assessed competence domains.

CONCLUSIONS

A growing number of studies have attempted to apply ML techniques to physician competence assessment. Although many studies have investigated the feasibility of certain techniques, more validation research is needed. The use of ML techniques may have the potential to integrate and analyze pragmatic information that could be used in real-time assessments and interventions.

Getting to grips with endoscopy - Learning endoscopic surgical skills induces bi-hemispheric plasticity of the grasping network

Endoscopic surgery requires skilled bimanual use of complex instruments that extend the peri-personal workspace. To delineate brain structures involved in learning such surgical skills, 48 medical students without surgical experience were randomly assigned to five training sessions on a virtual-reality endoscopy simulator or to a non-training group. Brain activity was probed with functional MRI while participants performed endoscopic tasks. Repeated task performance in the scanner was sufficient to enhance task-related activity in left ventral premotor cortex (PMv) and the anterior Intraparietal Sulcus (aIPS). Simulator training induced additional increases in task-related activation in right PMv and aIPS and reduced effective connectivity from left to right PMv. Skill improvement after training scaled with stronger task-related activation of the lateral left primary motor hand area (M1-HAND). The results suggest that a bilateral fronto-parietal grasping network and left M1-HAND are engaged in bimanual learning of tool-based manipulations in an extended peri-personal space.

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.

Systematic review of measurement tools to assess surgeons' intraoperative cognitive workload

BACKGROUND

Surgeons in the operating theatre deal constantly with high-demand tasks that require simultaneous processing of a large amount of information. In certain situations, high cognitive load occurs, which may impact negatively on a surgeon's performance. This systematic review aims to provide a comprehensive understanding of the different methods used to assess surgeons' cognitive load, and a critique of the reliability and validity of current assessment metrics.

METHODS

A search strategy encompassing MEDLINE, Embase, Web of Science, PsycINFO, ACM Digital Library, IEEE Xplore, PROSPERO and the Cochrane database was developed to identify peer-reviewed articles published from inception to November 2016. Quality was assessed by using the Medical Education Research Study Quality Instrument (MERSQI). A summary table was created to describe study design, setting, specialty, participants, cognitive load measures and MERSQI score.

RESULTS

Of 391 articles retrieved, 84 met the inclusion criteria, totalling 2053 unique participants. Most studies were carried out in a simulated setting (59 studies, 70 per cent). Sixty studies (71 per cent) used self-reporting methods, of which the NASA Task Load Index (NASA-TLX) was the most commonly applied tool (44 studies, 52 per cent). Heart rate variability analysis was the most used real-time method (11 studies, 13 per cent).

CONCLUSION

Self-report instruments are valuable when the aim is to assess the overall cognitive load in different surgical procedures and assess learning curves within competence-based surgical education. When the aim is to assess cognitive load related to specific operative stages, real-time tools should be used, as they allow capture of cognitive load fluctuation. A combination of both subjective and objective methods might provide optimal measurement of surgeons' cognition.

A decade of imaging surgeons' brain function (part II): A systematic review of applications for technical and nontechnical skills assessment

BACKGROUND

Functional neuroimaging technologies enable assessment of operator brain function and can deepen our understanding of skills learning, ergonomic optima, and cognitive processes in surgeons. Although there has been a critical mass of data detailing surgeons' brain function, this literature has not been reviewed systematically.

METHODS

A systematic search of original neuroimaging studies assessing surgeons' brain function and published up until November 2016 was conducted using Medline, Embase, and PsycINFO databases.

RESULTS

Twenty-seven studies fulfilled the inclusion criteria, including 3 feasibility studies, 14 studies exploring the neural correlates of technical skill acquisition, and the remainder investigating brain function in the context of intraoperative decision-making (n = 1), neurofeedback training (n = 1), robot-assisted technology (n = 5), and surgical teaching (n = 3). Early stages of learning open surgical tasks (knot-tying) are characterized by prefrontal cortical activation, which subsequently attenuates with deliberate practice. However, with complex laparoscopic skills (intracorporeal suturing), prefrontal cortical engagement requires substantial training, and attenuation occurs over a longer time course, after years of refinement. Neurofeedback and interventions that improve neural efficiency may enhance technical performance and skills learning.

CONCLUSION

Imaging surgeons' brain function has identified neural signatures of expertise that might help inform objective assessment and selection processes. Interventions that improve neural efficiency may target skill-specific brain regions and augment surgical performance.

A decade of imaging surgeons' brain function (part I): Terminology, techniques, and clinical translation

BACKGROUND

Functional neuroimaging has the potential to deepen our understanding of technical and nontechnical skill acquisition in surgeons, particularly as established assessment tools leave unanswered questions about inter-operator differences in ability that seem independent of experience.

METHODS

In this first of a 2-part article, we aim to utilize our experience in neuroimaging surgeons to orientate the nonspecialist reader to the principles of brain imaging. Terminology commonly used in brain imaging research is explained, placing emphasis on the "activation response" to an surgical task and its effect on local cortical hemodynamic parameters (neurovascular coupling).

RESULTS

Skills learning and subsequent consolidation and refinement through practice lead to reorganization of the functional architecture of the brain (known as "neuroplasticity"), evidenced by changes in the strength of regional activation as well as alterations in connectivity between brain regions, culminating in more efficient use of neural resources during task performance.

CONCLUSION

Currently available neuroimaging techniques that either directly (ie, measure electrical activity) or indirectly (ie, measure tissue hemodynamics) assess brain function are discussed. Finally, we highlight the important practical considerations when conducting brain imaging research in surgeons.

Application of Functional Near-Infrared Spectroscopy to the Study of Brain Function in Humans and Animal Models

Functional near-infrared spectroscopy (fNIRS) is a noninvasive optical imaging technique that indirectly assesses neuronal activity by measuring changes in oxygenated and deoxygenated hemoglobin in tissues using near-infrared light. fNIRS has been used not only to investigate cortical activity in healthy human subjects and animals but also to reveal abnormalities in brain function in patients suffering from neurological and psychiatric disorders and in animals that exhibit disease conditions. Because of its safety, quietness, resistance to motion artifacts, and portability, fNIRS has become a tool to complement conventional imaging techniques in measuring hemodynamic responses while a subject performs diverse cognitive and behavioral tasks in test settings that are more ecologically relevant and involve social interaction. In this review, we introduce the basic principles of fNIRS and discuss the application of this technique in human and animal studies.

Investigation of optical neuro-monitoring technique for detection of maintenance and emergence states during general anesthesia

The American Society of Anesthesiologist recommends peripheral physiological monitoring during general anesthesia, which offers no information regarding the effects of anesthetics on the brain. Since no "gold standard" method exists for this evaluation, such a technique is needed to ensure patient comfort, procedure quality and safety. In this study we investigated functional near infrared spectroscopy (fNIRS) as possible monitor of anesthetic effects on the prefrontal cortex. Anesthetic drugs, such as sevoflurane, suppress the cerebral metabolism and alter the cerebral blood flow. We hypothesize that fNIRS derived features carry information on the effects of anesthetics on the prefrontal cortex (PFC) that can be used for the classification of the anesthetized state. In this study, patients were continuously monitored using fNIRS, BIS and standard monitoring during surgical procedures under sevoflurane general anesthesia. Maintenance and emergence states were identified and fNIRS features were identified and compared between states. Linear and non-linear machine learning algorithms were investigated as methods for the classification of maintenance/emergence. The results show that changes in oxygenated (HbO₂) and deoxygenated hemoglobin (HHb) concentration and blood volume measured by fNIRS were associated with the transition between maintenance and emergence that occurs as a result of sevoflurane washout. We observed that during maintenance the signal is relatively more stable than during emergence. Maintenance and emergence states were classified with 94.7% accuracy with a non-linear model using the locally derived mean total hemoglobin, standard deviation of HbO₂, minimum and range of HbO₂ and HHb as features. These features were found to be correlated with the effects of sevoflurane and to carry information that allows real time and automatic classification of the anesthetized state with high accuracy.