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The need for automatic surgical skills assessment is increasing, especiallybecause manual feedback from senior surgeons observing junior surgeons is proneto subjectivity and time consuming. Thus, automating surgical skills evaluationis a very important step towards improving surgical practice. In this paper, wedesigned a Convolutional Neural Network (CNN) to evaluate surgeon skills byextracting patterns in the surgeon motions performed in robotic surgery. Theproposed method is validated on the JIGSAWS dataset and achieved verycompetitive results with 100% accuracy on the suturing and needle passingtasks. While we leveraged from the CNNs efficiency, we also managed to mitigateits black-box effect using class activation map. This feature allows our methodto automatically highlight which parts of the surgical task influenced theskill prediction and can be used to explain the classification and to providepersonalized feedback to the trainee.

Evaluating surgical skills from kinematic data using convolutional neural networks