Impact of virtual reality training on mastoidectomy performance: a prospective randomised study.

PURPOSE: The opportunities for surgical training and practice in the operating room are in decline due to limited resources, increased efficiency demands, growing complexity of the cases, and concerns for patient safety. Virtual reality (VR) offers a novel opportunity to enhance surgical training and provide complementary three-dimensional experience that has been usually available in the operating room. Since VR allows viewing and manipulation of realistic 3D models, the VR environment could enhance anatomical and topographical knowledge, in particular. In this study, we explored whether incorporating VR anatomy training improves novices' performance during mastoidectomy over traditional methods. METHODS: Thirty medical students were randomized into two groups and taught mastoidectomy in a structured manner. One group utilized a VR temporal bone model during the training while the other group used more traditional materials such as anatomy books. After the training, all participants completed a mastoidectomy on a 3D-printed temporal bone model under expert supervision. Performance during the mastoidectomy was evaluated with multiple metrics and feedback regarding the two training methods was gathered from the participants. RESULTS: The VR training method was rated better by the participants, and they also needed less guidance during the mastoidectomy. There were no significant differences in operational time, the occurrence of injuries, self-assessment scores, and the surgical outcome between the two groups. CONCLUSION: Our results support the utilization of VR training in complete novices as it has higher trainee satisfaction and leads to at least as good results as the more traditional methods.

Understanding Collective Human Behavior in Social Media Networks Via the Dynamical Hypothesis: Applications to Radicalization and Conspiratorial Beliefs.

The dynamical hypothesis has served to explore the ways in which cognitive agents can be understood dynamically and considered dynamical systems. Originally used to explain simple physical systems as a metaphor for cognition (i.e., the Watt governor) and eventually more complex animal systems (e.g., bird flocks), we argue that the dynamical hypothesis is among the most viable approaches to understanding pressing modern-day issues that arise from collective human behavior in online social networks. First, we discuss how the dynamical hypothesis is positioned to describe, predict, and explain the time-evolving nature of complex systems. Next, we adopt an interdisciplinary perspective to describe how online social networks are appropriately understood as dynamical systems. We introduce a dynamical modeling approach to reveal information about emergent properties in social media, where radicalized conspiratorial beliefs arise via coordination between user-level and community-level comments. Lastly, we contrast how the dynamical hypothesis differs from alternatives in explaining collective human behavior in social networks.

Hyperspectral placenta dataset: Hyperspectral image acquisition, annotations, and processing of biological tissues in microsurgical training.

The dataset consists of 101 hyperspectral images of four human placentas and six hyperspectral images of contrast dyes (i.e., indocyanine green and red and blue food colorant) that were captured in the range 515-900 nm, step = 5 nm. The hyperspectral images were manually annotated, delineating the key anatomical structures: arteries, veins, stroma, and the umbilical cord. Standard reference materials were used for flat-field correction. The dataset is instrumental for advancing machine-learning algorithms and automated classification of anatomical structures, particularly the classification of superficial and deep vessels and transparent tissue layers.

Hyperspectral Imaging in Brain Tumor Surgery-Evidence of Machine Learning-Based Performance.

BACKGROUND: Hyperspectral imaging (HSI) has the potential to enhance surgical tissue detection and diagnostics. Definite utilization of intraoperative HSI guidance demands validated machine learning and public datasets that currently do not exist. Moreover, current imaging conventions are dispersed, and evidence-based paradigms for neurosurgical HSI have not been declared. METHODS: We presented the rationale and a detailed clinical paradigm for establishing microneurosurgical HSI guidance. In addition, a systematic literature review was conducted to summarize the current indications and performance of neurosurgical HSI systems, with an emphasis on machine learning-based methods. RESULTS: The published data comprised a few case series or case reports aiming to classify tissues during glioma operations. For a multitissue classification problem, the highest overall accuracy of 80% was obtained using deep learning. Our HSI system was capable of intraoperative data acquisition and visualization with minimal disturbance to glioma surgery. CONCLUSIONS: In a limited number of publications, neurosurgical HSI has demonstrated unique capabilities in contrast to the established imaging techniques. Multidisciplinary work is required to establish communicable HSI standards and clinical impact. Our HSI paradigm endorses systematic intraoperative HSI data collection, which aims to facilitate the related standards, medical device regulations, and value-based medical imaging systems.

Detection improvement of gliomas in hyperspectral imaging of protoporphyrin IX fluorescence - in vitro comparison of visual identification and machine thresholds.

BACKGROUND: 5-aminolevulinic acid (5-ALA) - precursor of protoporphyrin IX (PpIX) - is utilized in fluorescence guided surgery (FGS) of high-grade gliomas. PpIX is used to identify traces of glioma during resection. Visual inspection of the fluorescence seems inaccurate in comparison to optic techniques such as hyperspectral imaging (HSI). AIM: To characterize the limits of PpIX fluorescence detection of (i) visual evaluation and (ii) HSI analysis and to (iii) develop a classification system for visible and non-visible PpIX fluorescence. METHODS: Samples with increasing concentrations (C) of PpIX and non-fluorescent controls were evaluated using a surgical microscope under blue light illumination. Similar samples were imaged with a HSI system tuned to PpIX fluorescence peak wavelength (635 nm) and control (RGB) channels. Samples' intensities were defined, leading to 96 analysed pixels after batching. RESULTS: Three expert neurosurgeons assessed the PpIX samples (n = 16) and controls (n = 8) with unanimous decisions (ICC = 0.704), resulting in 63% recognition rate, 48% sensitivity, 92% specificity, 92% positive predictive value (PPV) and 47% negative predictive value (NPV). HSI image analysis, comparing mean relative values, resulted in 96%, 100%, 86%, 94%, 100%, respectively. Minimum PpIX concentration detection for experts was 0.6-1.8 µmol/l and HSI's 0.03-0.15 µmol/l. CONCLUSIONS: PpIX concentrations of low-grade gliomas, and those reported on glioblastoma infiltration zones, are below experts' detection threshold. HSI analysis exceeds the performance of expert's visual inspection nearly by 20-fold. Hybrid FGS-HSI systems should be investigated in parallel to long-term outcomes. Described methods are applicable as a standard for calibration, testing and development of subvisual FGS techniques.

Capturing Sequences of Learners' Self-Regulatory Interactions With Instructional Material During Game-Based Learning Using Auto-Recurrence Quantification Analysis.

Undergraduate students (N = 82) learned about microbiology with Crystal Island, a game-based learning environment (GBLE), which required participants to interact with instructional materials (i.e., books and research articles, non-player character [NPC] dialogue, posters) spread throughout the game. Participants were randomly assigned to one of two conditions: full agency, where they had complete control over their actions, and partial agency, where they were required to complete an ordered play-through of Crystal Island. As participants learned with Crystal Island, log-file and eye-tracking time series data were collected to pinpoint instances when participants interacted with instructional materials. Hierarchical linear growth models indicated relationships between eye gaze dwell time and (1) the type of representation a learner gathered information from (i.e., large sections of text, poster, or dialogue); (2) the ability of the learner to distinguish relevant from irrelevant information; (3) learning gains; and (4) agency. Auto-recurrence quantification analysis (aRQA) revealed the degree to which repetitive sequences of interactions with instructional material were random or predictable. Through hierarchical modeling, analyses suggested that greater dwell times and learning gains were associated with more predictable sequences of interaction with instructional materials. Results from hierarchical clustering found that participants with restricted agency and more recurrent action sequences had greater learning gains. Implications are provided for how learning unfolds over learners' time in game using a non-linear dynamical systems analysis and the extent to which it can be supported within GBLEs to design advanced learning technologies to scaffold self-regulation during game play.

Eye-Tracking Indicators of Workload in Surgery: A Systematic Review.

BackgroundEye tracking is a powerful tool for unobtrusive and real time assessment of workload in clinical settings. Before the complex eye tracking derived surrogates can be proactively utilized to improve surgical safety, the indications, validity and reliability requires careful evaluation.MethodsWe conducted a systematic review of literature from 2010 to 2020 according to PRISMA guidelines. A search on PubMed, Cochrane, Scopus, Web of science, PsycInfo and Google scholar databases was conducted on July 2020. The following search query was used" ("eye tracking" OR "gaze tracking") AND (surgery OR surgical OR operative OR intraoperative) AND (workload OR stress)". Short papers, no peer reviewed or papers in which eye-tracking methodology was not used to investigate workload or stress factors in surgery, were omitted.ResultsA total of 17 (N = 17) studies were identified eligible to this review. Most of the studies (n = 15) measured workload in simulated setting. Task difficulty and expertise were the most studied factors. Studies consistently showed surgeon's eye movements such as pupil responses, gaze patterns, blinks were associated with the level of perceived workload. However, differences between measurements in operational room and simulated environments have been found.ConclusionPupil responses, blink rate and gaze indices are valid indicators of workload. However, the effect of distractions and non-technical factors on workload is underrepresented aspect in the literature even though recognized as underlying factors in successful surgery.

Combined Gaze Metrics as Stress-Sensitive Indicators of Microsurgical Proficiency.

Background. Evaluation of microsurgical proficiency is conventionally subjective, time consuming, and unreliable. Eye movement-based metrics have been promising not only in detection of surgical expertise but also in identifying actual cognitive stress and workload. We investigated if pupil dilations and blinks could be utilized in parallel to accurately classify microsurgical proficiency and its moderating features, especially task-related stress. Methods. Participants (n = 11) were divided into groups based on prior experience in microsurgery: novices (n = 6) with no experience and trained microsurgeons (n = 5). All participants conducted standardized suturing tasks with authentic instruments and a surgical microscope. A support vector machine classifier was used to classify features of microsurgical expertise based on percentage changes in pupil size. Results. A total of 109 successful sutures with 1090 segments were recorded. Classification of expertise from sutures achieved accuracies between 74.3% and 76.0%. Classification from individual segments based on these same features was not feasible. Conclusions. Combined gaze metrics are applicable for classifying surgical proficiency during a defined task. Pupil dilation is also sensitive to external stress factors; however, the usefulness of blinks is impaired by low blink rates. The results can be translated to surgical education to improve feedback and should be investigated individually in the context of actual performance and in real patient operations. Combined gaze metrics may be ultimately utilized to help microsurgeons monitor their performance and workload in real time-which may lead to prevention of errors.

Beyond Dyadic Coordination: Multimodal Behavioral Irregularity in Triads Predicts Facets of Collaborative Problem Solving.

We hypothesize that effective collaboration is facilitated when individuals and environmental components form a synergy where they work together and regulate one another to produce stable patterns of behavior, or regularity, as well as adaptively reorganize to form new behaviors, or irregularity. We tested this hypothesis in a study with 32 triads who collaboratively solved a challenging visual computer programming task for 20 min following an introductory warm-up phase. Multidimensional recurrence quantification analysis was used to examine fine-grained (i.e., every 10 s) collective patterns of regularity across team members' speech rate, body movement, and team interaction with the shared user interface. We found that teams exhibited significant patterns of regularity as compared to shuffled baselines, but there were no systematic trends in regularity across time. We also found that periods of regularity were associated with a reduction in overall behavior. Notably, the production of irregular behavior predicted expert-coded metrics of collaborative activity, such as teams' ability to construct shared knowledge and effectively negotiate and coordinate execution of solutions, net of overall behavioral production and behavioral self-similarity. Our findings support the theory that groups can interact to form interpersonal synergies and indicate that information about system-level dynamics is a viable way to understand and predict effective collaborative processes.