High density optical neuroimaging predicts surgeons's subjective experience and skill levels

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.

Development of performance and learning rate evaluation models in robot-assisted surgery using electroencephalography and eye-tracking

The existing performance evaluation methods in robot-assisted surgery (RAS) are mainly subjective, costly, and affected by shortcomings such as the inconsistency of results and dependency on the raters' opinions. The aim of this study was to develop models for an objective evaluation of performance and rate of learning RAS skills while practicing surgical simulator tasks. The electroencephalogram (EEG) and eye-tracking data were recorded from 26 subjects while performing Tubes, Suture Sponge, and Dots and Needles tasks. Performance scores were generated by the simulator program. The functional brain networks were extracted using EEG data and coherence analysis. Then these networks, along with community detection analysis, facilitated the extraction of average search information and average temporal flexibility features at 21 Brodmann areas (BA) and four band frequencies. Twelve eye-tracking features were extracted and used to develop linear random intercept models for performance evaluation and multivariate linear regression models for the evaluation of the learning rate. Results showed that subject-wise standardization of features improved the R2 of the models. Average pupil diameter and rate of saccade were associated with performance in the Tubes task (multivariate analysis; p-value = 0.01 and p-value = 0.04, respectively). Entropy of pupil diameter was associated with performance in Dots and Needles task (multivariate analysis; p-value = 0.01). Average temporal flexibility and search information in several BAs and band frequencies were associated with performance and rate of learning. The models may be used to objectify performance and learning rate evaluation in RAS once validated with a broader sample size and tasks.

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.

Technical skill assessment in minimally invasive surgery using artificial intelligence: a systematic review

BACKGROUND

Technical skill assessment in surgery relies on expert opinion. Therefore, it is time-consuming, costly, and often lacks objectivity. Analysis of intraoperative data by artificial intelligence (AI) has the potential for automated technical skill assessment. The aim of this systematic review was to analyze the performance, external validity, and generalizability of AI models for technical skill assessment in minimally invasive surgery.

METHODS

A systematic search of Medline, Embase, Web of Science, and IEEE Xplore was performed to identify original articles reporting the use of AI in the assessment of technical skill in minimally invasive surgery. Risk of bias (RoB) and quality of the included studies were analyzed according to Quality Assessment of Diagnostic Accuracy Studies criteria and the modified Joanna Briggs Institute checklists, respectively. Findings were reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement.

RESULTS

In total, 1958 articles were identified, 50 articles met eligibility criteria and were analyzed. Motion data extracted from surgical videos (n = 25) or kinematic data from robotic systems or sensors (n = 22) were the most frequent input data for AI. Most studies used deep learning (n = 34) and predicted technical skills using an ordinal assessment scale (n = 36) with good accuracies in simulated settings. However, all proposed models were in development stage, only 4 studies were externally validated and 8 showed a low RoB.

CONCLUSION

AI showed good performance in technical skill assessment in minimally invasive surgery. However, models often lacked external validity and generalizability. Therefore, models should be benchmarked using predefined performance metrics and tested in clinical implementation studies.

Decoding Mental Effort in a Quasi-Realistic Scenario: A Feasibility Study on Multimodal Data Fusion and Classification

Humans' performance varies due to the mental resources that are available to successfully pursue a task. To monitor users' current cognitive resources in naturalistic scenarios, it is essential to not only measure demands induced by the task itself but also consider situational and environmental influences. We conducted a multimodal study with 18 participants (nine female, M = 25.9 with SD = 3.8 years). In this study, we recorded respiratory, ocular, cardiac, and brain activity using functional near-infrared spectroscopy (fNIRS) while participants performed an adapted version of the warship commander task with concurrent emotional speech distraction. We tested the feasibility of decoding the experienced mental effort with a multimodal machine learning architecture. The architecture comprised feature engineering, model optimisation, and model selection to combine multimodal measurements in a cross-subject classification. Our approach reduces possible overfitting and reliably distinguishes two different levels of mental effort. These findings contribute to the prediction of different states of mental effort and pave the way toward generalised state monitoring across individuals in realistic applications.

Multimodal Approach to Assess a Virtual Reality-based Surgical Training Platform

Virtual reality (VR) can bring numerous benefits to the learning process. Combining a VR environment with physiological sensors can be beneficial in skill assessment. We aim to investigate trainees' physiological (ECG) and behavioral differences during the virtual reality-based surgical training environment. Our finding showed a significant association between the VR-Score and all participants' total NASA-TLX workload score. The extent of the NASA-TLX workload score was negatively correlated with VR-Score (R2 =0.15, P < 0.03). In time-domain ECG analysis, we found that RMSSD (R2 =0.16, P < 0.05) and pNN50 (R2 =0.15, P < 0.05) scores correlated with significantly higher VR-score of all participants. In this study, we used SVM (linear kernel) and Logistic Regression classification techniques to classify the participants as gamers and non-gamers using data from VR headsets. Both SVM and Logistic Regression accurately classified the participants as gamers and non-gamers with 83% accuracy. For both SVM and Linear Regression, precision was noted as 88%, recall as 83%, and f1-score as 83%. There is increasing interest in characterizing trainees' physiological and behavioral activity profiles in a VR environment, aiming to develop better training and assessment methodologies.

Video game experience affects performance, cognitive load, and brain activity in laparoscopic surgery training

Objectives

Video games can be a valuable tool for surgery training. Individuals who interact or play video games tend to have a better visuospatial ability when compared to non-gamers. Numerous studies suggest that video game experience is associated with faster acquisition, greater sharpening, and longer retention of laparoscopic skills. Given the neurocognitive complexity of surgery skill, multimodal approaches are required to understand how video game playing enhances laparoscopy skill.

Material and Methods

Twenty-seven students with no laparoscopy experience and varying levels of video game experience performed standard laparoscopic training tasks. Their performance, subjective cognitive loading, and prefrontal cortical activity were recorded and analyzed. As a reference point to use in comparing the two novice groups, we also included data from 13 surgeons with varying levels of laparoscopy experience and no video game experience.

Results

Results indicated that video game experience was correlated with higher performance (R2 = 0.22, p <0.01) and lower cognitive load (R2 = 0.21, p <0.001), and the prefrontal cortical activation of students with gaming experience was relatively lower than those without gaming experience. In terms of these variables, gaming experience in novices tended to produce effects similar to those of laparoscopy experience in surgeons.

Conclusion

Our results suggest that along the dimensions of performance, cognitive load, and brain activity, the effects of video gaming experience on novice laparoscopy trainees are similar to those of real-world laparoscopy experience on surgeons. We believe that the neural underpinnings of surgery skill and its links with gaming experience need to be investigated further using wearable functional brain imaging.

The Evidence Basis for Learning Theory and Technology in Surgical Skills Training

Orthopaedic trainees face a complex and challenging training environment that is currently becoming more competency driven. Associated with these changes are an increasing introduction and use of a variety of technologically driven surgical training augments. Although these new learning resources can positively transform the educational environment, they must be used appropriately by both learners and educators. To aid in this, we review learning theories because they apply to surgical skills training and highlight recent surgical training evidence that demonstrates how technology use can be optimized to promote surgical learning, with an emphasis on procedural learning theory and cognitive load theory. Specifically, we review the evidence demonstrating the importance of targeting technology to a learner's experience level and methods to optimize cognitive load by managing intrinsic load, minimizing extraneous load, and maximizing germane load.

Wearable, high-density fNIRS and diffuse optical tomography technologies: a perspective

Recent progress in optoelectronics has made wearable and high-density functional near-infrared spectroscopy (fNIRS) and diffuse optical tomography (DOT) technologies possible for the first time. These technologies have the potential to open new fields of real-world neuroscience by enabling functional neuroimaging of the human cortex at a resolution comparable to fMRI in almost any environment and population. In this perspective article, we provide a brief overview of the history and the current status of wearable high-density fNIRS and DOT approaches, discuss the greatest ongoing challenges, and provide our thoughts on the future of this remarkable technology.

Measurement and Management of Cognitive Load in Surgical Education: A Narrative Review

BACKGROUND

Cognitive load should be considered in designing optimal educational programs in health care. Despite the highly demanding nature of surgery and surgical education, a consensus on how to manage cognitive load has not been established. The objective of this review is to map out how recent surgical education research incorporates cognitive load.

METHODS

A literature search was performed using keywords related to cognitive load and digital education up to December 2021. Studies published in English relevant to assessment and management of cognitive load in surgical education were included. Terminology, assessment tools, association with different surgical procedures and training modalities, and programs considering cognitive load were reported.

RESULTS

We identified several terms to describe cognitive load. Cognitive load was measured by subjective, self-reported questionnaires and by objective measurements, such as physiological parameters or estimated by reaction time to secondary tasks. Subjective measurements reported cognitive load in one or multiple dimensions. Correlations between subjective and objective measurements were shown in multiple studies. Overall, higher cognitive load was observed in training for more complex tasks and high-fidelity modalities, and among less experienced trainees. Cognitive load theory has been lately incorporated into designing teaching programs.

CONCLUSIONS

A broad range of terms and assessment tools were identified for cognitive load. To maximize the learning outcome, management of cognitive load is necessary in surgical education. This review summarizes the current knowledge in assessment and management of cognitive load in surgical education and provides suggestions for future studies.

Electroencephalography can provide advance warning of technical errors during laparoscopic surgery

BACKGROUND

Intraoperative adverse events lead to patient injury and death, and are increasing. Early warning systems (EWSs) have been used to detect patient deterioration and save lives. However, few studies have used EWSs to monitor surgical performance and caution about imminent technical errors. Previous (non-surgical) research has investigated neural activity to predict future motor errors using electroencephalography (EEG). The present proof-of-concept cohort study investigates whether EEG could predict technical errors in surgery.

METHODS

In a large academic hospital, three surgical fellows performed 12 elective laparoscopic general surgeries. Audiovisual data of the operating room and the surgeon's neural activity were recorded. Technical errors and epochs of good surgical performance were coded into events. Neural activity was observed 40 s prior and 10 s after errors and good events to determine how far in advance errors were detected. A hierarchical regression model was used to account for possible clustering within surgeons. This prospective, proof-of-concept, cohort study was conducted from July to November 2021, with a pilot period from February to March 2020 used to optimize the technique of data capture and included participants who were blinded from study hypotheses.

RESULTS

Forty-five technical errors, mainly due to too little force or distance (n = 39), and 27 good surgical events were coded during grasping and dissection. Neural activity representing error monitoring (p = .008) and motor uncertainty (p = .034) was detected 17 s prior to errors, but not prior to good surgical performance.

CONCLUSIONS

These results show that distinct neural signatures are predictive of technical error in laparoscopic surgery. If replicated with low false-alarm rates, an EEG-based EWS of technical errors could be used to improve individualized surgical training by flagging imminent unsafe actions-before errors occur and cause patient harm.

Screening for Alzheimer's disease using prefrontal resting-state functional near-infrared spectroscopy

INTRODUCTION

Alzheimer's disease (AD) is neurodegenerative dementia that causes neurovascular dysfunction and cognitive impairment. Currently, 50 million people live with dementia worldwide, and there are nearly 10 million new cases every year. There is a need for relatively less costly and more objective methods of screening and early diagnosis.

METHODS

Functional near-infrared spectroscopy (fNIRS) systems are a promising solution for the early Detection of AD. For a practical clinically relevant system, a smaller number of optimally placed channels are clearly preferable. In this study, we investigated the number and locations of the best-performing fNIRS channels measuring prefrontal cortex activations. Twenty-one subjects diagnosed with AD and eighteen healthy controls were recruited for the study.

RESULTS

We have shown that resting-state fNIRS recordings from a small number of prefrontal locations provide a promising methodology for detecting AD and monitoring its progression. A high-density continuous-wave fNIRS system was first used to verify the relatively lower hemodynamic activity in the prefrontal cortical areas observed in patients with AD. By using the episode averaged standard deviation of the oxyhemoglobin concentration changes as features that were fed into a Support Vector Machine; we then showed that the accuracy of subsets of optical channels in predicting the presence and severity of AD was significantly above chance. The results suggest that AD can be detected with a 0.76 sensitivity score and a 0.68 specificity score while the severity of AD could be detected with a 0.75 sensitivity score and a 0.72 specificity score with ≤5 channels.

DISCUSSION

These scores suggest that fNIRS is a viable technology for conveniently detecting and monitoring AD as well as investigating underlying mechanisms of disease progression.

Artificial Intelligence and Surgical Education: A Systematic Scoping Review of Interventions

OBJECTIVE

To synthesize peer-reviewed evidence related to the use of artificial intelligence (AI) in surgical education DESIGN: We conducted and reported a scoping review according to the standards outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analysis with extension for Scoping Reviews guideline and the fourth edition of the Joanna Briggs Institute Reviewer's Manual. We systematically searched eight interdisciplinary databases including MEDLINE-Ovid, ERIC, EMBASE, CINAHL, Web of Science: Core Collection, Compendex, Scopus, and IEEE Xplore. Databases were searched from inception until the date of search on April 13, 2021.

SETTING/PARTICIPANTS

We only examined original, peer-reviewed interventional studies that self-described as AI interventions, focused on medical education, and were relevant to surgical trainees (defined as medical or dental students, postgraduate residents, or surgical fellows) within the title and abstract (see Table 2). Animal, cadaveric, and in vivo studies were not eligible for inclusion.

RESULTS

After systematically searching eight databases and 4255 citations, our scoping review identified 49 studies relevant to artificial intelligence in surgical education. We found diverse interventions related to the evaluation of surgical competency, personalization of surgical education, and improvement of surgical education materials across surgical specialties. Many studies used existing surgical education materials, such as the Objective Structured Assessment of Technical Skills framework or the JHU-ISI Gesture and Skill Assessment Working Set database. Though most studies did not provide outcomes related to the implementation in medical schools (such as cost-effective analyses or trainee feedback), there are numerous promising interventions. In particular, many studies noted high accuracy in the objective characterization of surgical skill sets. These interventions could be further used to identify at-risk surgical trainees or evaluate teaching methods.

CONCLUSIONS

There are promising applications for AI in surgical education, particularly for the assessment of surgical competencies, though further evidence is needed regarding implementation and applicability.

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.