Dynamic changes of brain functional states during surgical skill acquisition

Shafiei, Somayeh B.; Hussein, Ahmed A.

doi:10.1371/journal.pone.0204836

Cited by 13 publications

(14 citation statements)

References 48 publications

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“…Functional brain network reconfigures by practice throughout skill acquisition [ 62 ]. Although subjects have different levels of expertise, all of them had required skills to perform considered gestures on the patient, with high performance and under the supervision of a master surgeon in the OR.…”

Section: Discussionmentioning

confidence: 99%

Surgical Hand Gesture Recognition Utilizing Electroencephalogram as Input to the Machine Learning and Network Neuroscience Algorithms

Shafiei

Durrani

Jing

et al. 2021

Sensors

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Surgical gestures detection can provide targeted, automated surgical skill assessment and feedback during surgical training for robot-assisted surgery (RAS). Several sources including surgical videos, robot tool kinematics, and an electromyogram (EMG) have been proposed to reach this goal. We aimed to extract features from electroencephalogram (EEG) data and use them in machine learning algorithms to classify robot-assisted surgical gestures. EEG was collected from five RAS surgeons with varying experience while performing 34 robot-assisted radical prostatectomies over the course of three years. Eight dominant hand and six non-dominant hand gesture types were extracted and synchronized with associated EEG data. Network neuroscience algorithms were utilized to extract functional brain network and power spectral density features. Sixty extracted features were used as input to machine learning algorithms to classify gesture types. The analysis of variance (ANOVA) F-value statistical method was used for feature selection and 10-fold cross-validation was used to validate the proposed method. The proposed feature set used in the extra trees (ET) algorithm classified eight gesture types performed by the dominant hand of five RAS surgeons with an accuracy of 90%, precision: 90%, sensitivity: 88%, and also classified six gesture types performed by the non-dominant hand with an accuracy of 93%, precision: 94%, sensitivity: 94%.

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Section: Discussionmentioning

confidence: 99%

Surgical Hand Gesture Recognition Utilizing Electroencephalogram as Input to the Machine Learning and Network Neuroscience Algorithms

Shafiei

Durrani

Jing

et al. 2021

Sensors

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“…39 40 This behavior is the product of a distributed network of neural circuitry that takes a complex sequence of action selection and planned motor execution plan and refines over time to ensure smooth and seemingly automatic performance. 41 42 However, there have been very few investigations into the neural processes linking brain and behavior in the surgical domain, [43][44][45][46][47][48][49][50][51] with technological constraints limiting the ability to probe the neural underpinnings of surgical performance.…”

Section: Discussionmentioning

confidence: 99%

Frontal theta brain activity varies as a function of surgical experience and task error

Balkhoyor¹,

Awais

Biyani³

et al. 2020

BMJ Surg Interv Health Technologies

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ObjectiveInvestigations into surgical expertise have almost exclusively focused on overt behavioral characteristics with little consideration of the underlying neural processes. Recent advances in neuroimaging technologies, for example, wireless, wearable scalp-recorded electroencephalography (EEG), allow an insight into the neural processes governing performance. We used scalp-recorded EEG to examine whether surgical expertise and task performance could be differentiated according to an oscillatory brain activity signal known as frontal theta—a putative biomarker for cognitive control processes.Design, setting, and participantsBehavioral and EEG data were acquired from dental surgery trainees with 1 year (n=25) and 4 years of experience (n=20) while they performed low and high difficulty drilling tasks on a virtual reality surgical simulator. EEG power in the 4–7 Hz range in frontal electrodes (indexing frontal theta) was examined as a function of experience, task difficulty and error rate.ResultsFrontal theta power was greater for novices relative to experts (p=0.001), but did not vary according to task difficulty (p=0.15) and there was no Experience × Difficulty interaction (p=0.87). Brain–behavior correlations revealed a significant negative relationship between frontal theta and error in the experienced group for the difficult task (r=−0.594, p=0.0058), but no such relationship emerged for novices.ConclusionWe find frontal theta power differentiates between surgical experiences but correlates only with error rates for experienced surgeons while performing difficult tasks. These results provide a novel perspective on the relationship between expertise and surgical performance.

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“…We partitioned each adjacency matrix, calculated for every second of each recording, into communities (functional states) using the multilayer modularity maximization criteria [ 33 , 34 ]. The expression of a community refers to the phenomenon where brain regions assigned to the same community are more likely to be strongly connected to one another as compared to regions assigned to different communities [ 35 ].…”

Section: Methodsmentioning

confidence: 99%

“…A generalized Louvain-like “greedy” algorithm and Newman–Girvan (NG) null network were used for modularity maximization and community detection [ 33 , 34 ]. A consensus iterative algorithm with 100 repetitions was used to decrease the nondeterministic effect of the community detection algorithm [ 34 , 36 ].…”

Section: Methodsmentioning

confidence: 99%

Association between Functional Brain Network Metrics and Surgeon Performance and Distraction in the Operating Room

et al. 2021

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Objective: The aim of this work was to examine (electroencephalogram) EEG features that represent dynamic changes in the functional brain network of a surgical trainee and whether these features can be used to evaluate a robot assisted surgeon’s (RAS) performance and distraction level in the operating room. Materials and Methods: Electroencephalogram (EEG) data were collected from three robotic surgeons in an operating room (OR) via a 128-channel EEG headset with a frequency of 500 samples/second. Signal processing and network neuroscience algorithms were applied to the data to extract EEG features. The SURG-TLX and NASA-TLX metrics were subjectively evaluated by a surgeon and mentor at the end of each task. The scores given to performance and distraction metrics were used in the analyses here. Statistical test data were utilized to select EEG features that have a significant relationship with surgeon performance and distraction while carrying out a RAS surgical task in the OR. Results: RAS surgeon performance and distraction had a relationship with the surgeon’s functional brain network metrics as recorded throughout OR surgery. We also found a significant negative Pearson correlation between performance and the distraction level (−0.37, p-value < 0.0001). Conclusions: The method proposed in this study has potential for evaluating RAS surgeon performance and the level of distraction. This has possible applications in improving patient safety, surgical mentorship, and training.

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Dynamic changes of brain functional states during surgical skill acquisition

Cited by 13 publications

References 48 publications

Surgical Hand Gesture Recognition Utilizing Electroencephalogram as Input to the Machine Learning and Network Neuroscience Algorithms

Surgical Hand Gesture Recognition Utilizing Electroencephalogram as Input to the Machine Learning and Network Neuroscience Algorithms

Frontal theta brain activity varies as a function of surgical experience and task error

Association between Functional Brain Network Metrics and Surgeon Performance and Distraction in the Operating Room

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