Queen's School of Computing Supervisors: Gabor Fichtinger, Tamas Ungi
Chair: David Skillicorn
Internal Examiner: James Stewart
External Examiner: Pascal Fallavollita, University of Ottawa, Faculty of Health Sciences


COMPUTER-ASSISTED WORKFLOW RECOGNITION FOR CENTRAL VENOUS CATHETERIZATION

Abstract

Purpose: The transition to competency-based medical education from an apprenticeship model requires a greater amount of human resources to evaluate trainees. Therefore, there is an increased need for tools that can evaluate trainees and provide feedback without the need for an expert observer. This thesis describes the development of a system for training central venous catheterization. The procedure includes many tasks that must be performed in a predetermined order. This system, Central Line Tutor, is able to provide trainees with feedback about their performance by evaluating their compliance to proper workflow when practicing in a simulated setting.
Methods: Central Line Tutor uses a combination of electromagnetic tracking and tool recognition from a webcam video to analyze trainees’ workflow compliance. Critical tasks such as inserting the needle into the vessel are recognized by tracking the tools’ positions. The remaining workflow tasks were recognized by identifying the tools used in the given tasks. In the proof of concept implementation of the system, we used a color-based approach. This method has some limitations including: insufficient accuracy, and a long setup time. To improve Central Line Tutor’s task recognition, we also implemented a more robust method for tool recognition that involved training a convolutional neural network. We evaluated two different networks, Inception-V3 and MobileNet, and compared their accuracy to the initial color-based approach.