Surgical skill level classification model development using EEG and eye-gaze data and machine learning algorithms

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Abstract

The aim of this study was to develop machine learning classification models using electroencephalogram (EEG) and eye-gaze features to predict the level of surgical expertise in robot-assisted surgery (RAS). EEG and eye-gaze data were recorded from 11 participants who performed cystectomy, hysterectomy, and nephrectomy using the da Vinci robot. Skill level was evaluated by an expert RAS surgeon using the modified Global Evaluative Assessment of Robotic Skills (GEARS) tool, and data from three subtasks were extracted to classify skill levels using three classification models—multinomial logistic regression (MLR), random forest (RF), and gradient boosting (GB). The GB algorithm was used with a combination of EEG and eye-gaze data to classify skill levels, and differences between the models were tested using two-sample t tests. The GB model using EEG features showed the best performance for blunt dissection (83% accuracy), retraction (85% accuracy), and burn dissection (81% accuracy). The combination of EEG and eye-gaze features using the GB algorithm improved the accuracy of skill level classification to 88% for blunt dissection, 93% for retraction, and 86% for burn dissection. The implementation of objective skill classification models in clinical settings may enhance the RAS surgical training process by providing objective feedback about performance to surgeons and their teachers.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11701-023-01722-8.

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Author and article information

Contributors

Somayeh B. Shafiei:

ORCID: http://orcid.org/0000-0002-9256-6284

Somayeh.BesharatShafiei@RoswellPark.org

Journal

Journal ID (nlm-ta): J Robot Surg

Journal ID (iso-abbrev): J Robot Surg

Title: Journal of Robotic Surgery

Publisher: Springer London (London )

ISSN (Print): 1863-2483

ISSN (Electronic): 1863-2491

Publication date (Electronic): 21 October 2023

Publication date PMC-release: 21 October 2023

Publication date (Print): 2023

Volume: 17

Issue: 6

Pages: 2963-2971

Affiliations

[1 ]Intelligent Cancer Care Laboratory, Department of Urology, Roswell Park Comprehensive Cancer Center, Buffalo, NY 14263 USA

[2 ]Department of Animal Biosciences, University of Guelph, ( https://ror.org/01r7awg59) Guelph, ON N1G 2W1 Canada

[3 ]GRID grid.240614.5, ISNI 0000 0001 2181 8635, Department of Urology, ; Roswell Park Comprehensive Cancer Center, Buffalo, NY 14263 USA

[4 ]Mike and Sugar Barnes Faculty Fellow II, Wm Michael Barnes and Department of Industrial and Systems Engineering at Texas A&M University, ( https://ror.org/01f5ytq51) College Station, TX 77843 USA

[5 ]Obstetrics and Gynecology Residency Program, Sisters of Charity Health System, ( https://ror.org/02xare716) Buffalo, NY 14214 USA

Author information

Somayeh B. Shafiei http://orcid.org/0000-0002-9256-6284

Saeed Shadpour http://orcid.org/0000-0002-2842-031X

Article

Publisher ID: 1722

DOI: 10.1007/s11701-023-01722-8

PMC ID: 10678814

PubMed ID: 37864129

SO-VID: 4047f46b-9a7e-41b3-8726-b92aa210b5d3

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 19 May 2023

Date accepted : 19 August 2023

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ScienceOpen disciplines: Surgery

Keywords: blunt dissection,retraction,burn dissection,hysterectomy,cystectomy,nephrectomy,robot-assisted surgery,expertise level

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ScienceOpen disciplines: Surgery

Keywords: blunt dissection, retraction, burn dissection, hysterectomy, cystectomy, nephrectomy, robot-assisted surgery, expertise level

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Surgical skill level classification model development using EEG and eye-gaze data and machine learning algorithms

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Karger: Dermatology

Most cited references 26

The brain's default network: anatomy, function, and relevance to disease.

Dynamic reconfiguration of human brain networks during learning.

Defining and identifying communities in networks.

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