Eric Fichtel1, Nathan Lau2, Juyeon Park3, Sarah Henrickson Parker4, Siddarth Ponnala5, Shimae Fitzgibbons6, Shawn D Safford7. 1. Grado Department of Industrial and Systems Engineering, Virginia Tech, 546 Whittemore Hall, 1185 Perry Street, Blacksburg, VA, 24061, USA. 2. Grado Department of Industrial and Systems Engineering, Virginia Tech, 546 Whittemore Hall, 1185 Perry Street, Blacksburg, VA, 24061, USA. nathan.lau@vt.edu. 3. Virginia Tech Carilion School of Medicine and Carilion Clinic, Virginia Tech, Roanoke, USA. 4. Virginia Tech Carilion Research Institute, Virginia Tech, Roanoke, USA. 5. Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, USA. 6. Department of Surgery, MedStar Georgetown University Hospital, Washington, DC, USA. 7. Department of Surgery, Virginia Tech Carilion School of Medicine and Carilion Clinic, Virginia Tech, Roanoke, USA.
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.
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.
Entities:
Keywords:
Area of interest; Expertise; Eye tracking; Laparoscopic surgery; Surgical events
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