@conference {ODriscoll2022a, title = {Feasibility of object detection for skill assessment in central venous catheterization}, booktitle = {SPIE Medical Imaging}, year = {2022}, month = {02/2022}, publisher = {SPIE Medical Imaging}, organization = {SPIE Medical Imaging}, address = {San Diego}, abstract = {

Purpose: Computer-assisted surgical skill assessment methods have traditionally relied on tracking tool motion with physical sensors. These tracking systems can be expensive, bulky, and impede tool function. Recent advances in object detection networks have made it possible to quantify tool motion using only a camera. These advances open the door for a low-cost alternative to current physical tracking systems for surgical skill assessment. This study determines the feasibility of using metrics computed with object detection by comparing them to widely accepted metrics computed using traditional tracking methods in central venous catheterization. Methods: Both video and tracking data were recorded from participants performing central venous catheterization on a venous access phantom. A Faster Region-Based Convolutional Neural Network was trained to recognize the ultrasound probe and syringe on the video data. Tracking-based metrics were computed using the Perk Tutor extension of 3D Slicer. The path length and usage time for each tool were then computed using both the video and tracking data. The metrics from object detection and tracking were compared using Spearman rank correlation. Results: The path lengths had a rank correlation coefficient of 0.22 for the syringe (p\<0.03) and 0.35 (p\<0.001) for the ultrasound probe. For the usage times, the correlation coefficient was 0.37 (p\<0.001) for the syringe and 0.34 (p\<0.001) for the ultrasound probe. Conclusions: The video-based metrics correlated significantly with the tracked metrics, suggesting that object detection could be a feasible skill assessment method for central venous catheterization.

}, keywords = {central venous catheterization, computer-assisted skill assessment, Object detection}, author = {Olivia O{\textquoteright}Driscoll and R. Hisey and M. Holden and Daenis Camire and Jason Erb and Daniel Howes and Tamas Ungi and Gabor Fichtinger} }