  Mastering the technical skills required to perform surgery is an extremely
challenging task. Video-based assessment allows surgeons to receive feedback on
their technical skills to facilitate learning and development. Currently, this
feedback comes primarily from manual video review, which is time-intensive and
limits the feasibility of tracking a surgeon's progress over many cases. In
this work, we introduce a motion-based approach to automatically assess
surgical skills from surgical case video feed. The proposed pipeline first
tracks surgical tools reliably to create motion trajectories and then uses
those trajectories to predict surgeon technical skill levels. The tracking
algorithm employs a simple yet effective re-identification module that improves
ID-switch compared to other state-of-the-art methods. This is critical for
creating reliable tool trajectories when instruments regularly move on- and
off-screen or are periodically obscured. The motion-based classification model
employs a state-of-the-art self-attention transformer network to capture short-
and long-term motion patterns that are essential for skill evaluation. The
proposed method is evaluated on an in-vivo (Cholec80) dataset where an
expert-rated GOALS skill assessment of the Calot Triangle Dissection is used as
a quantitative skill measure. We compare transformer-based skill assessment
with traditional machine learning approaches using the proposed and
state-of-the-art tracking. Our result suggests that using motion trajectories
from reliable tracking methods is beneficial for assessing surgeon skills based
solely on video streams.
