  Five billion people in the world lack access to quality surgical care.
Surgeon skill varies dramatically, and many surgical patients suffer
complications and avoidable harm. Improving surgical training and feedback
would help to reduce the rate of complications, half of which have been shown
to be preventable. To do this, it is essential to assess operative skill, a
process that currently requires experts and is manual, time consuming, and
subjective. In this work, we introduce an approach to automatically assess
surgeon performance by tracking and analyzing tool movements in surgical
videos, leveraging region-based convolutional neural networks. In order to
study this problem, we also introduce a new dataset, m2cai16-tool-locations,
which extends the m2cai16-tool dataset with spatial bounds of tools. While
previous methods have addressed tool presence detection, ours is the first to
not only detect presence but also spatially localize surgical tools in
real-world laparoscopic surgical videos. We show that our method both
effectively detects the spatial bounds of tools as well as significantly
outperforms existing methods on tool presence detection. We further demonstrate
the ability of our method to assess surgical quality through analysis of tool
usage patterns, movement range, and economy of motion.
