TY - JOUR
T1 - Eye tracking in surgical education
T2 - gaze-based dynamic area of interest can discriminate adverse events and expertise
AU - Fichtel, Eric
AU - Lau, Nathan
AU - Park, Juyeon
AU - Henrickson Parker, Sarah
AU - Ponnala, Siddarth
AU - Fitzgibbons, Shimae
AU - Safford, Shawn D.
N1 - Funding Information:
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).
PY - 2019/7/15
Y1 - 2019/7/15
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85055493113&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85055493113&partnerID=8YFLogxK
U2 - 10.1007/s00464-018-6513-5
DO - 10.1007/s00464-018-6513-5
M3 - Article
C2 - 30341656
AN - SCOPUS:85055493113
SN - 0930-2794
VL - 33
SP - 2249
EP - 2256
JO - Surgical endoscopy
JF - Surgical endoscopy
IS - 7
ER -