  Purpose: Manual feedback from senior surgeons observing less experienced
trainees is a laborious task that is very expensive, time-consuming and prone
to subjectivity. With the number of surgical procedures increasing annually,
there is an unprecedented need to provide an accurate, objective and automatic
evaluation of trainees' surgical skills in order to improve surgical practice.
Methods: In this paper, we designed a convolutional neural network (CNN) to
classify surgical skills by extracting latent patterns in the trainees' motions
performed during robotic surgery. The method is validated on the JIGSAWS
dataset for two surgical skills evaluation tasks: classification and
regression. Results: Our results show that deep neural networks constitute
robust machine learning models that are able to reach new competitive
state-of-the-art performance on the JIGSAWS dataset. While we leveraged from
CNNs' efficiency, we were able to minimize its black-box effect using the class
activation map technique. Conclusions: This characteristic allowed our method
to automatically pinpoint which parts of the surgery influenced the skill
evaluation the most, thus allowing us to explain a surgical skill
classification and provide surgeons with a novel personalized feedback
technique. We believe this type of interpretable machine learning model could
integrate within "Operation Room 2.0" and support novice surgeons in improving
their skills to eventually become experts.
