Literature DB >> 26864145

Technical mentorship during robot-assisted surgery: a cognitive analysis.

Ahmed A Hussein1,2, Somayeh B Shafiei3, Mohamed Sharif1, Ehsan Esfahani3, Basel Ahmad1, Justen D Kozlowski1, Zishan Hashmi1, Khurshid A Guru1.   

Abstract

OBJECTIVE: To investigate cognitive and mental workload assessments, which may play a critical role in defining successful mentorship.
MATERIALS AND METHODS: The 'Mind Maps' project aimed at evaluating cognitive function with regard to surgeon's expertise and trainee's skills. The study included electroencephalogram (EEG) recordings of a mentor observing trainee surgeons in 20 procedures involving extended lymph node dissection (eLND) or urethrovesical anastomosis (UVA), with simultaneous assessment of trainees using the National Aeronautics and Space Administration Task Load index (NASA-TLX) questionnaire. We also compared the brain activity of the mentor during this study with his own brain activity while actually performing the same surgical steps from previous procedures populated in the 'Mind Maps' project.
RESULTS: During eLND and UVA, when the mentor thought the trainee's mental demand and effort were low based on his NASA-TLX questionnaire (not satisfied with his performance), his EEG-based mental workload increased (reflecting more concern and attention). The mentor was mentally engaged and concerned while he was engrossed in observing the surgery. This was further supported by the finding that there was no significant difference in the mental demands and workload between observing and operating for the expert surgeon.
CONCLUSIONS: This study objectively evaluated the cognitive engagement of a surgical mentor teaching technical skills during surgery. The study provides a deeper understanding of how surgical teaching actually works and opens new horizons for assessment and teaching of surgery. Further research is needed to study the feasibility of this novel concept in assessment and guidance of surgical performance.
© 2016 The Authors BJU International © 2016 BJU International Published by John Wiley & Sons Ltd.

Entities:  

Keywords:  EEG; assessment; cognition; robot-assisted; surgery; training

Mesh:

Year:  2016        PMID: 26864145     DOI: 10.1111/bju.13445

Source DB:  PubMed          Journal:  BJU Int        ISSN: 1464-4096            Impact factor:   5.588


  7 in total

1.  Systematic review of measurement tools to assess surgeons' intraoperative cognitive workload.

Authors:  R D Dias; M C Ngo-Howard; M T Boskovski; M A Zenati; S J Yule
Journal:  Br J Surg       Date:  2018-02-21       Impact factor: 6.939

2.  Anticipation, teamwork and cognitive load: chasing efficiency during robot-assisted surgery.

Authors:  Kevin Sexton; Amanda Johnson; Amanda Gotsch; Ahmed A Hussein; Lora Cavuoto; Khurshid A Guru
Journal:  BMJ Qual Saf       Date:  2017-07-08       Impact factor: 7.035

Review 3.  Non-Technical Skill Assessment and Mental Load Evaluation in Robot-Assisted Minimally Invasive Surgery.

Authors:  Renáta Nagyné Elek; Tamás Haidegger
Journal:  Sensors (Basel)       Date:  2021-04-10       Impact factor: 3.576

Review 4.  Use of neuroimaging to measure neurocognitive engagement in health professions education: a scoping review.

Authors:  Serkan Toy; Dana D Huh; Joshua Materi; Julie Nanavati; Deborah A Schwengel
Journal:  Med Educ Online       Date:  2022-12

5.  Functional Brain States Measure Mentor-Trainee Trust during Robot-Assisted Surgery.

Authors:  Somayeh B Shafiei; Ahmed Aly Hussein; Sarah Feldt Muldoon; Khurshid A Guru
Journal:  Sci Rep       Date:  2018-02-26       Impact factor: 4.379

6.  Dynamic changes of brain functional states during surgical skill acquisition.

Authors:  Somayeh B Shafiei; Ahmed Aly Hussein; Khurshid A Guru
Journal:  PLoS One       Date:  2018-10-31       Impact factor: 3.240

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

Authors:  Somayeh B Shafiei; Mohammad Durrani; Zhe Jing; Michael Mostowy; Philippa Doherty; Ahmed A Hussein; Ahmed S Elsayed; Umar Iqbal; Khurshid Guru
Journal:  Sensors (Basel)       Date:  2021-03-03       Impact factor: 3.576

  7 in total

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