Insights From Surgeons' Eye-Movement Data in a Virtual Simulation Surgical Training Environment: Effect of Experience Level and Hand Conditions

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Date

2018

Journal Title

Journal ISSN

Volume Title

Publisher

Taylor & Francis Ltd

Open Access Color

Green Open Access

Yes

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Publicly Funded

No
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Top 10%
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Average
Popularity
Top 10%

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Abstract

Today, with the advancements in the eye-tracking technology, it has become possible to follow surgeons' eye movements while performing surgical tasks. Despite the availability of studies providing a better understanding of surgeons' eye movements, research in the particular field of endoneurosurgery is very limited. Analysing surgeons' eye-movement data can provide general insights into how to improve surgical education programmes. In this study, four simulation-based task-oriented endoscopic surgery training scenarios were developed and implemented by 23 surgical residents using three different hand conditions; dominant, non-dominant, and both. The participants' recorded eye data comprised fixation number, fixation duration, saccade number, saccade duration, pursuit number, pursuit duration, and pupil size. This study has two main contributions: First, it reports on the eye-movement behaviours of surgical residents, demonstrating that novice residents tended to make more fixations and saccades than intermediate residents. They also had a higher fixation duration and followed the objects more frequently compared to the intermediates. Furthermore, hand conditions significantly affected the eye movements of the participants. Based on these results, it can be concluded that eye-movement data can be used to assess the skill levels of surgical residents and would be an important measure to better guide trainees in surgical education programmes. The second contribution of this study is the eye-movement event classifications of 10 different algorithms. Although the algorithms mostly provided similar results, there were a few conflicted values for some classifications, which offers a clue as to how researchers can utilise these algorithms with low sampling frequency eye trackers.

Description

Menekse Dalveren, Gonca Gokce/0000-0002-8649-1909; Cagiltay, Nergiz/0000-0003-0875-9276

Keywords

Eye tracking, eye-movement classification, experience level, hand condition, surgical virtual environments, endoneurosurgery

Turkish CoHE Thesis Center URL

Fields of Science

05 social sciences, 0501 psychology and cognitive sciences

Citation

WoS Q

Q2

Scopus Q

Q1
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OpenCitations Citation Count
20

Source

Behaviour & Information Technology

Volume

37

Issue

5

Start Page

517

End Page

537

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Citations

CrossRef : 11

Scopus : 27

Captures

Mendeley Readers : 90

SCOPUS™ Citations

27

checked on Feb 07, 2026

Web of Science™ Citations

23

checked on Feb 07, 2026

Page Views

1

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