  The goal of this study was to develop new reliable open surgery suturing
simulation system for training medical students in situation where resources
are limited or in the domestic setup. Namely, we developed an algorithm for
tools and hands localization as well as identifying the interactions between
them based on simple webcam video data, calculating motion metrics for
assessment of surgical skill. Twenty-five participants performed multiple
suturing tasks using our simulator. The YOLO network has been modified to a
multi-task network, for the purpose of tool localization and tool-hand
interaction detection. This was accomplished by splitting the YOLO detection
heads so that they supported both tasks with minimal addition to computer
run-time. Furthermore, based on the outcome of the system, motion metrics were
calculated. These metrics included traditional metrics such as time and path
length as well as new metrics assessing the technique participants use for
holding the tools. The dual-task network performance was similar to that of two
networks, while computational load was only slightly bigger than one network.
In addition, the motion metrics showed significant differences between experts
and novices. While video capture is an essential part of minimally invasive
surgery, it is not an integral component of open surgery. Thus, new algorithms,
focusing on the unique challenges open surgery videos present, are required. In
this study, a dual-task network was developed to solve both a localization task
and a hand-tool interaction task. The dual network may be easily expanded to a
multi-task network, which may be useful for images with multiple layers and for
evaluating the interaction between these different layers.
