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

Somayeh B Shafiei,Ahmed A Hussein,Umar Iqbal,Mohammad Durrani,Zhe Jing,Khurshid Guru,Sena Arman,Kristopher Attwood

doi:10.3390/brainsci11040468

Somayeh B Shafiei, Ahmed A Hussein + Show 6 more

Open Access

https://doi.org/10.3390/brainsci11040468

Copy DOI

Journal: Brain sciences	Publication Date: Apr 8, 2021
Citations: 8	License type: CC BY 4.0

Affiliation: Roswell Park Cancer Institute

Abstract

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.

Highlights

Robot-assisted surgery (RAS) offers advantages such as improved three-dimensionality for surgery, magnified images of the work area, and improved dexterity compared to the traditional surgical framework
The analysis showed that the risk of local recurrence increased, and disease-specific survival was lower in patients treated both by non-specialist colorectal surgeons and by surgeons performing less than 21 procedures during the study
We used a random intercept model to test the differences between surgeons and our results show no differences between surgeons (p = 0.38)

Summary

Introduction

Robot-assisted surgery (RAS) offers advantages such as improved three-dimensionality for surgery, magnified images of the work area, and improved dexterity compared to the traditional surgical framework. While the advantages of RAS are appreciated, the limitations of the robotic user interface and the steep learning curve [1,2] are factors that contribute to a lower utilization of robot-assisted technologies. Even in areas where RAS is widely used, such as gynecology and urology, the outcomes in RAS seem to predominantly correlate with the level of expertise of the individual surgeon [3,4]. Even the performance of expert surgeons may be poor in certain surgical situations, i.e., cases with intra-operative challenges [5,6]. Several factors in a surgical environment can affect a surgeon’s performance [8,9].

Methods

Results

Conclusion