  Purpose: A profound education of novice surgeons is crucial to ensure that
surgical interventions are effective and safe. One important aspect is the
teaching of technical skills for minimally invasive or robot-assisted
procedures. This includes the objective and preferably automatic assessment of
surgical skill. Recent studies presented good results for automatic, objective
skill evaluation by collecting and analyzing motion data such as trajectories
of surgical instruments. However, obtaining the motion data generally requires
additional equipment for instrument tracking or the availability of a robotic
surgery system to capture kinematic data. In contrast, we investigate a method
for automatic, objective skill assessment that requires video data only. This
has the advantage that video can be collected effortlessly during minimally
invasive and robot-assisted training scenarios.
  Methods: Our method builds on recent advances in deep learning-based video
classification. Specifically, we propose to use an inflated 3D ConvNet to
classify snippets, i.e., stacks of a few consecutive frames, extracted from
surgical video. The network is extended into a Temporal Segment Network during
training.
  Results: We evaluate the method on the publicly available JIGSAWS dataset,
which consists of recordings of basic robot-assisted surgery tasks performed on
a dry lab bench-top model. Our approach achieves high skill classification
accuracies ranging from 95.1% to 100.0%.
  Conclusions: Our results demonstrate the feasibility of deep learning-based
assessment of technical skill from surgical video. Notably, the 3D ConvNet is
able to learn meaningful patterns directly from the data, alleviating the need
for manual feature engineering. Further evaluation will require more annotated
data for training and testing.
