Surgical Endoscopy

, Volume 33, Issue 7, pp 2249–2256 | Cite as

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

  • Eric Fichtel
  • Nathan LauEmail author
  • Juyeon Park
  • Sarah Henrickson Parker
  • Siddarth Ponnala
  • Shimae Fitzgibbons
  • Shawn D. Safford



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.


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.


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.


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


Eye tracking Laparoscopic surgery Area of interest Expertise Surgical events 



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


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).

Compliance with ethical standards


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.


  1. 1.
    von Strauss und Torney M, Dell-Kuster S, Mechera R, Rosenthal R, Langer I (2012) The cost of surgical training: analysis of operative time for laparoscopic cholecystectomy. Surg Endosc 26(9):2579–2586. CrossRefGoogle Scholar
  2. 2.
    Williams TE Jr, Satiani B, Thomas A, Ellison EC (2009) The impending shortage and the estimated cost of training the future surgical workforce. Ann Surg 250(4):590–597Google Scholar
  3. 3.
    Fraher EP, Knapton A, Sheldon GF, Meyer A, Ricketts TC (2013) Projecting surgeon supply using a dynamic model. Ann Surg 257(5):867–872CrossRefGoogle Scholar
  4. 4.
    Sheldon GF (2007) Surgical workforce since the 1975 study of surgical services in the United States: an update. Ann Surg 246(4):541–545CrossRefGoogle Scholar
  5. 5.
    Khan RSA, Tien G, Atkins MS, Zheng B, Panton ONM, Meneghetti AT (2012) Analysis of eye gaze: do novice surgeons look at the same location as expert surgeons during a laparoscopic operation? Surg Endosc 26(12):3536–3540. CrossRefGoogle Scholar
  6. 6.
    Wilson M, McGrath J, Vine S, Brewer J, Defriend D, Masters R (2010) Psychomotor control in a virtual laparoscopic surgery training environment: gaze control parameters differentiate novices from experts. Surg Endosc 24(10):2458–2464. CrossRefGoogle Scholar
  7. 7.
    Tien T, Pucher PH, Sodergren MH, Sriskandarajah K, Yang G-Z, Darzi A (2014) Eye tracking for skills assessment and training: a systematic review. J Surg Res 191(1):169–178. CrossRefGoogle Scholar
  8. 8.
    Gegenfurtner A, Siewiorek A, Lehtinen E, Säljö R (2013) Assessing the quality of expertise differences in the comprehension of medical visualizations. Vocat Learn 6(1):37–54. CrossRefGoogle Scholar
  9. 9.
    Wilson MR, Vine SJ, Bright E, Masters RSW, Defriend D, McGrath JS (2011) Gaze training enhances laparoscopic technical skill acquisition and multi-tasking performance: a randomized, controlled study. Surg Endosc 25(12):3731–3739. CrossRefGoogle Scholar
  10. 10.
    Vine SJ, Masters RSW, McGrath JS, Bright E, Wilson MR (2012) Cheating experience: guiding novices to adopt the gaze strategies of experts expedites the learning of technical laparoscopic skills. Surgery 152(1):32–40. CrossRefGoogle Scholar
  11. 11.
    Sodergren MH, Orihuela-Espina F, Froghi F, Clark J, Teare J, Yang GZ, Darzi A (2011) Value of orientation training in laparoscopic cholecystectomy. BJS 98(10):1437–1445. CrossRefGoogle Scholar
  12. 12.
    Hermens F, Flin R, Ahmed I (2013) Eye movements in surgery: a literature review. J Eye Mov Res 6(4):1–11Google Scholar
  13. 13.
    Holmqvist K, Nystrom M, Andersson R, Dewhurst R, Jarodzka H, van de Weijer J (2011) Eye tracking: a comprehensive guide to methods and measures. Oxford University Press, Oxford, New YorkGoogle Scholar
  14. 14.
    Goldberg JH, Helfman JI (2010) Comparing information graphics: a critical look at eye tracking. Paper presented at the proceedings of the 3rd BELIV’10 workshop: BEyond time and errors: novel evaLuation methods for Information Visualization, Atlanta, GeorgiaGoogle Scholar
  15. 15.
    Sodergren MH, Orihuela-Espina F, Mountney P, Clark J, Teare J, Darzi A, Yang G-Z (2011) Orientation strategies in natural orifice translumenal endoscopic surgery. Ann Surg 254(2):257–266CrossRefGoogle Scholar
  16. 16.
    Eivazi S, Bednarik R, Tukiainen M, Fraunberg Mvuz, Leinonen V, Jääskeläinen JE (2012) Gaze behaviour of expert and novice microneurosurgeons differs during observations of tumor removal recordings. Paper presented at the proceedings of the symposium on eye tracking research and applications, Santa Barbara, CaliforniaGoogle Scholar
  17. 17.
    Wilson MR, McGrath JS, Vine SJ, Brewer J, Defriend D, Masters RSW (2011) Perceptual impairment and psychomotor control in virtual laparoscopic surgery. Surg Endosc 25(7):2268–2274. CrossRefGoogle Scholar
  18. 18.
    Ahmidi N, Ishii M, Fichtinger G, Gallia Gary L, Hager Gregory D (2012) An objective and automated method for assessing surgical skill in endoscopic sinus surgery using eye-tracking and tool-motion data. Int Forum Allergy Rhinol 2(6):507–515. CrossRefGoogle Scholar
  19. 19.
    Richstone L, Schwartz MJ, Seideman C, Cadeddu J, Marshall S, Kavoussi LR (2010) Eye metrics as an objective assessment of surgical skill. Ann Surg 252(1):177–182CrossRefGoogle Scholar
  20. 20.
    Hessels RS, Kemner C, van den Boomen C, Hooge ITC (2016) The area-of-interest problem in eyetracking research: a noise-robust solution for face and sparse stimuli. Behav Res Methods 48(4):1694–1712. CrossRefGoogle Scholar
  21. 21.
    Tien G, Atkins MS, Zheng B (2012) Measuring gaze overlap on videos between multiple observers. In: Proceedings of the symposium on eye tracking research and applications. ACM, Santa Barbara, CA, pp 309–312.
  22. 22.
    Gegenfurtner A, Lehtinen E, Säljö R (2011) Expertise differences in the comprehension of visualizations: a meta-analysis of eye-tracking research in professional domains. Educ Psychol Rev 23(4):523–552. CrossRefGoogle Scholar
  23. 23.
    Orquin JL, Ashby NJS, Clarke ADF (2015) Areas of interest as a signal detection problem in behavioral eye-tracking research. J Behav Decis Mak 29(2–3):103–115. Google Scholar
  24. 24.
    Di Stasi LL, Diaz-Piedra C, Rieiro H, Sánchez Carrión JM, Martin Berrido M, Olivares G, Catena A (2016) Gaze entropy reflects surgical task load. Surg Endosc 30(11):5034–5043. CrossRefGoogle Scholar
  25. 25.
    Law B, Atkins MS, Kirkpatrick AE, Lomax AJ (2004) Eye gaze patterns differentiate novice and experts in a virtual laparoscopic surgery training environment. Paper presented at the proceedings of the 2004 symposium on eye tracking research & applications, San Antonio, TexasGoogle Scholar
  26. 26.
    Kasarskis P, Stehwien J, Hickox J, Aretz A, Wickens CD (2001) Comparison of expert and novice scan behaviors during VFR flight. In: 11th international symposium on aviation psychology. The Ohio State University, Columbus, OHGoogle Scholar
  27. 27.
    Kundel HL, Nodine CF, Conant EF, Weinstein SP (2007) Holistic component of image perception in mammogram interpretation: gaze-tracking study. Radiology 242(2):396–402. CrossRefGoogle Scholar
  28. 28.
    Atkins SM, Jiang X, Tien G, Zheng B (2012) Saccadic delays on targets while watching videos. In: Association for computing machinery. Santa Barbara, CA, pp 405–408Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Grado Department of Industrial and Systems EngineeringVirginia TechBlacksburgUSA
  2. 2.Virginia Tech Carilion School of Medicine and Carilion ClinicVirginia TechRoanokeUSA
  3. 3.Virginia Tech Carilion Research InstituteVirginia TechRoanokeUSA
  4. 4.Department of Industrial and Systems EngineeringUniversity of Wisconsin-MadisonMadisonUSA
  5. 5.Department of SurgeryMedStar Georgetown University HospitalWashingtonUSA
  6. 6.Department of Surgery, Virginia Tech Carilion School of Medicine and Carilion ClinicVirginia TechRoanokeUSA

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