Electroencephalographic Monitoring of Brain Wave Activity During Laparoscopic Surgical Simulation to Measure Surgeon Concentration and Stress: Can the Student Become the Master?

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.

Neurocognitive Correlates of Clinical Decision Making: A Pilot Study Using Electroencephalography

The development of sound clinical reasoning, while essential for optimal patient care, can be quite an elusive process. Researchers typically rely on a self-report or observational measures to study decision making, but clinicians' reasoning processes may not be apparent to themselves or outside observers. This study explored electroencephalography (EEG) to examine neurocognitive correlates of clinical decision making during a simulated American Board of Anesthesiology-style standardized oral exam. Eight novice anesthesiology residents and eight fellows who had recently passed their board exams were included in the study. Measures included EEG recordings from each participant, demographic information, self-reported cognitive load, and observed performance. To examine neurocognitive correlates of clinical decision making, power spectral density (PSD) and functional connectivity between pairs of EEG channels were analyzed. Although both groups reported similar cognitive load (p = 0.840), fellows outperformed novices based on performance scores (p < 0.001). PSD showed no significant differences between the groups. Several coherence features showed significant differences between fellows and residents, mostly related to the channels within the frontal, between the frontal and parietal, and between the frontal and temporal areas. The functional connectivity patterns found in this study could provide some clues for future hypothesis-driven studies in examining the underlying cognitive processes that lead to better clinical reasoning.

A scoping review of emotions and related constructs in simulation-based education research articles

BACKGROUND

While acknowledgement of emotions' importance in simulation-based education is emerging, there are concerns regarding how education researchers understand the concept of emotions for them to deliberately incorporate emotionally charged scenarios into simulation-based education. This concern is highlighted especially in the context of medical education often lacking strong theoretical integration. To map out how current simulation-based education literature conceptualises emotion, we conducted a scoping review on how emotions and closely related constructs (e.g. stress, and emotional intelligence) are conceptualised in simulation-based education articles that feature medical students, residents, and fellows.

METHODS

The scoping review was based on articles published in the last decade identified through database searches (EMBASE and Medline) and hand-searched articles. Data extraction included the constructs featured in the articles, their definitions, instruments used, and the types of emotions captured. Only empirical articles were included (e.g. no review or opinion articles). Data were charted via descriptive analyses.

RESULTS

A total of 141 articles were reviewed. Stress was featured in 88 of the articles, while emotions and emotional intelligence were highlighted in 45 and 34 articles respectively. Conceptualisations of emotions lacked integration of theory. Measurements of emotions mostly relied on self-reports while stress was often measured via physiological and self-report measurements. Negative emotions such as anxiety were sometimes seen as interchangeable with the term stress. No inferences were made about specific emotions of participants from their emotional intelligence.

CONCLUSIONS

Our scoping review illustrates that learners in simulation-based education are most often anxious and fearful. However, this is partially due to medical education prioritising measuring negative emotions. Further theoretical integration when examining emotions and stress may help broaden the scope towards other kinds of emotions and better conceptualisations of their impact. We call for simulation education researchers to reflect on how they understand emotions, and whether their understanding may neglect any specific aspect of affective experiences their simulation participants may have.

Using electroencephalography to explore neurocognitive correlates of procedural proficiency: A pilot study to compare experts and novices during simulated endotracheal intubation

The objective of this study was to explore the use of EEG as a measure of neurocognitive engagement during a procedural task. In this observational study, self-reported cognitive load, observed performance, and EEG signatures in experts and novices were compared during simulated endotracheal intubation. Twelve medical students (novices) and eight senior anesthesiology trainees (experts) were included in the study. Experts reported significantly lower cognitive load (P < 0.001) and outperformed novices based on the observational checklist (P < 0.001). EEG signatures differed significantly between the experts and novices. Experts showed a greater increase in delta and theta band amplitudes, especially in temporal and frontal locations and in right occipital areas for delta. A machine learning algorithm showed 83.3 % accuracy for expert-novice skill classification using the selected EEG features. Performance scores were positively correlated (P < 0.05) with event-related amplitudes for delta and theta bands at locations where experts and novices showed significant differences. Increased delta and frontal/midline theta oscillations on EEG suggested that experts had better attentional control than novices. This pilot study provides initial evidence that EEG may be a useful, noninvasive measure of neurocognitive engagement in operational settings and that it has the potential to complement traditional clinical skills assessment.

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.

Stress in surgical educational environments: a systematic review

BACKGROUND

The effects of stress on surgical residents and how stress management training can prepare residents to effectively manage stressful situations is a relevant topic. This systematic review aimed to analyze the literature regarding (1) the current stress monitoring tools and their use in surgical environments, (2) the current methods in surgical stress management training, and (3) how stress affects surgical performance.

METHODS

A search strategy was implemented to retrieve relevant articles from Web of Science, Scopus, and PubMed. The 787 initially retrieved articles were reviewed for further evaluation according to the inclusion/exclusion criteria (Prospero registration number CRD42021252682).

RESULTS

Sixty-one articles were included in the review. The stress monitoring methods found in the articles showed heart rate analysis as the most used monitoring tool for physiological parameters while the STAI-6 scale was preferred for psychological parameters. The stress management methods found in the articles were mental-, simulation- and feedback-based training, with the mental-based training showing clear positive effects on participants. The studies analyzing the effects of stress on surgical performance showed both negative and positive effects on technical and non-technical performance.

CONCLUSIONS

The impact of stress responses presents an important factor in surgical environments, affecting residents' training and performance. This study identified the main methods used for monitoring stress parameters in surgical educational environments. The applied surgical stress management training methods were diverse and demonstrated positive effects on surgeons' stress levels and performance. There were negative and positive effects of stress on surgical performance, although a collective pattern on their effects was not clear.

The development of an eye movement-based deep learning system for laparoscopic surgical skills assessment

The development of valid, reliable, and objective methods of skills assessment is central to modern surgical training. Numerous rating scales have been developed and validated for quantifying surgical performance. However, many of these scoring systems are potentially flawed in their design in terms of reliability. Eye-tracking techniques, which provide a more objective investigation of the visual-cognitive aspects of the decision-making process, recently have been utilized in surgery domains for skill assessment and training, and their use has been focused on investigating differences between expert and novice surgeons to understand task performance, identify experienced surgeons, and establish training approaches. Ten graduate students at the National Taiwan University of Science and Technology with no prior laparoscopic surgical skills were recruited to perform the FLS peg transfer task. Then k-means clustering algorithm was used to split 500 trials into three dissimilar clusters, grouped as novice, intermediate, and expert levels, by an objective performance assessment parameter incorporating task duration with error score. Two types of data sets, namely, time series data extracted from coordinates of eye fixation and image data from videos, were used to implement and test our proposed skill level detection system with ensemble learning and a CNN algorithm. Results indicated that ensemble learning and the CNN were able to correctly classify skill levels with accuracies of 76.0% and 81.2%, respectively. Furthermore, the incorporation of coordinates of eye fixation and image data allowed the discrimination of skill levels with a classification accuracy of 82.5%. We examined more levels of training experience and further integrated an eye tracking technique and deep learning algorithms to develop a tool for objective assessment of laparoscopic surgical skill. With a relatively unbalanced sample, our results have demonstrated that the approach combining the features of visual fixation coordinates and images achieved a very promising level of performance for classifying skill levels of trainees.

Study of Human Tacit Knowledge Based on Electroencephalogram Signal Characteristics

Tacit knowledge is the kind of knowledge that is difficult to transfer to another person by means of writing it down or verbalizing it. In the mineral grinding process, the proficiency of the operators depends on the tacit knowledge gained from their experience and training rather than on knowledge learned from a handbook. This article proposed a method combining the electroencephalogram (EEG) signals and the industrial process to detect the proficiency of the operators in the mineral grinding process to reveal the effect of tacit knowledge on the functional cortical connection. The functional brain networks of operators were established based on partial direct coherence and directed transfer function of EEG, and the multi-classifiers were used with the graph-theoretic indexes of the FBNs as input to distinguish the trained operators (Hps) from the non-trained operators (Lps). The results showed that the brain networks of Hps had a better connectivity than those of Lps (p < 0.01), and the accuracy of classification was up to 94.2%. Our studies confirm that based on the performance of EEG features and the combination of industrial operational operation and cognitive processes, the proficiency of the operators can be detected.

Self-health monitoring and wearable neurotechnologies

Brain-computer interfaces and wearable neurotechnologies are now used to measure real-time neural and physiologic signals from the human body and hold immense potential for advancements in medical diagnostics, prevention, and intervention. Given the future role that wearable neurotechnologies will likely serve in the health sector, a critical state-of-the-art assessment is necessary to gain a better understanding of their current strengths and limitations. In this chapter we present wearable electroencephalography systems that reflect groundbreaking innovations and improvements in real-time data collection and health monitoring. We focus on specifications reflecting technical advantages and disadvantages, discuss their use in fundamental and clinical research, their current applications, limitations, and future directions. While many methodological and ethical challenges remain, these systems host the potential to facilitate large-scale data collection far beyond the reach of traditional research laboratory settings.

Robotics-Assisted Surgical Skills Evaluation based on Electrocortical Activity

Skills assessment in Robotics-Assisted Minimally Invasive Surgery (RAMIS) is mainly performed based on temporal, motion-based and outcome-based metrics. While these components are essential for the proper assessment of skills in RAMIS, they do not suffice for full representation of all underlying aspects of skilled performance. Besides such commonplace components of skills, there exist other elements to be taken into account for comprehensive skills assessment. Among such elements are cognitive states (such as levels of stress, attention, concentration) that can directly affect performance. Investigating the impact of electrocortical activity and cognitive states of RAMIS surgeons over their performance has, however, received little attention in the literature. Therefore, in this paper, novel performance metrics based on electroencephalography (EEG) signals are studied for potential augmentation into RAMIS training and its assessment platform. For this purpose, a user study was conducted involving 23 novices and 9 expert RAMIS surgeons. The participants were asked to perform two tasks on the dv-Trainer®, (Mimic Technologies) RAMIS simulator, while their brain EEG signals were being measured using the Muse EEG headband (InteraXon Inc.). The performance metrics were defined as mean values of band powers of EEG signals over various ranges of frequency. Statistical analysis was performed to evaluate metrics over 5 different ranges of frequency for 4 electrode locations and during 2 RAMIS training tasks. The results indicated statistically significant differences in electrocortical activity between novices and experts in temporoparietal and left frontal regions of their brain for mid to high-frequency ranges. Overall, RAMIS experts showed lower levels of electrocortical activity in those regions compared to novices. The results indicate that electrocortical activity measured by EEG signals have the potential to provide useful information for skills assessment in RAMIS.