Real-time workflow detection using webcam video for providing real-time feedback in central venous catheterization training

Rebecca Hisey; Tamas Ungi; Matthew Holden; Zachary Baum; Zsuzsanna Keri; Caitlin McCallum; Daniel W. Howes; Gabor Fichtinger

doi:10.1117/12.2293494

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13 March 2018 Real-time workflow detection using webcam video for providing real-time feedback in central venous catheterization training

Rebecca Hisey, Tamas Ungi, Matthew Holden, Zachary Baum, Zsuzsanna Keri, Caitlin McCallum, Daniel W. Howes, Gabor Fichtinger

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Proceedings Volume 10576, Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling; 1057620 (2018) https://doi.org/10.1117/12.2293494
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

Purpose: Medical schools are shifting from a time-based approach to a competency-based education approach. A competency-based approach requires continuous observation and evaluation of trainees. The goal of Central Line Tutor is to be able to provide instruction and real-time feedback for trainees learning the procedure of central venous catheterization, without requiring a continuous expert observer. The purpose of this study is to test the accuracy of the workflow detection method of Central Line Tutor. This study also looks at the effectiveness of object recognition from a webcam video for workflow detection. Methods: Five trials of the procedure were recorded from Central Line Tutor. Five reviewers were asked to identify the timestamp of the transition points in each recording. Reviewer timestamps were compared to those identified by Central Line Tutor. Differences between these values were used to calculate average transitional delay. Results: Central Line Tutor was able to identify 100% of transition points in the procedure with an average transitional delay of -1.46 ± 0.81s. The average transitional delay of EM and webcam tracked steps were -0.35 ± 2.51s and -2.46 ± 3.57s respectively. Conclusions: Central line tutor was able to detect completion of all workflow tasks with minimal delay and may be used to provide trainees with real-time feedback. The results also show that object recognition from a webcam video is an effective method for detecting workflow tasks in the procedure of central venous catheterization.

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Rebecca Hisey, Tamas Ungi, Matthew Holden, Zachary Baum, Zsuzsanna Keri, Caitlin McCallum, Daniel W. Howes, and Gabor Fichtinger "Real-time workflow detection using webcam video for providing real-time feedback in central venous catheterization training", Proc. SPIE 10576, Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling, 1057620 (13 March 2018); https://doi.org/10.1117/12.2293494

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