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Eye tracking in surgical education: gaze-based dynamic area of interest can discriminate adverse events and expertise

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Abstract

Background

Eye-gaze metrics derived from areas of interest (AOIs) have been suggested to be effective for surgical skill assessment. However, prior research is mostly based on static images and simulated tasks that may not translate to complex and dynamic surgical scenes. Therefore, eye-gaze metrics must advance to account for changes in the location of important information during a surgical procedure.

Methods

We developed a dynamic AOI generation technique based on eye gaze collected from an expert viewing surgery videos. This AOI updated as the gaze of the expert moved with changes in the surgical scene. This technique was evaluated through an experiment recruiting a total of 20 attendings and residents to view 10 videos associated with and another 10 without adverse events.

Results

Dwell time percentage (i.e., gaze duration) inside the AOI differentiated video type (U = 13508.5, p < 0.001) between videos with the presence (Mdn = 16.75) versus absence (Mdn = 19.95) of adverse events. This metric also differentiated participant group (U = 14029.5, p < 0.001) between attendings (Mdn = 15.45) and residents (Mdn = 19.80). This indicates that our dynamic AOIs reflecting the expert eye gaze was able to differentiate expertise, and the presence of unexpected adverse events.

Conclusion

This dynamic AOI generation technique produced dynamic AOIs for deriving eye-gaze metrics that were sensitive to expertise level and event characteristics.

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Acknowledgements

We are grateful to all the attendings and residents who volunteered to participate in this study.

Funding

This research was supported through a Center for Excellence in Surgical Education, Research and Training (CESERT) Grant of the Association for Surgical Education (#16-01), and a Research Acceleration Program Grant of Carilion Clinic (#65111).

Author information

Authors and Affiliations

  1. Grado Department of Industrial and Systems Engineering, Virginia Tech, 546 Whittemore Hall, 1185 Perry Street, Blacksburg, VA, 24061, USA

    Eric Fichtel & Nathan Lau

  2. Virginia Tech Carilion School of Medicine and Carilion Clinic, Virginia Tech, Roanoke, USA

    Juyeon Park

  3. Virginia Tech Carilion Research Institute, Virginia Tech, Roanoke, USA

    Sarah Henrickson Parker

  4. Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, USA

    Siddarth Ponnala

  5. Department of Surgery, MedStar Georgetown University Hospital, Washington, DC, USA

    Shimae Fitzgibbons

  6. Department of Surgery, Virginia Tech Carilion School of Medicine and Carilion Clinic, Virginia Tech, Roanoke, USA

    Shawn D. Safford

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  1. Eric Fichtel
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Corresponding author

Correspondence to Nathan Lau.

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Disclosures

Eric Fichtel, Nathan Lau, Sarah Hendrickson Parker, Siddarth Ponnala, Juyeon Park, Shimae Fitzgibbons, and Shawn D. Safford have no conflicts of interest or financial ties to disclose.

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Fichtel, E., Lau, N., Park, J. et al. Eye tracking in surgical education: gaze-based dynamic area of interest can discriminate adverse events and expertise. Surg Endosc 33, 2249–2256 (2019). https://doi.org/10.1007/s00464-018-6513-5

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  • DOI: https://doi.org/10.1007/s00464-018-6513-5

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