  Surgical training in medical school residency programs has followed the
apprenticeship model. The learning and assessment process is inherently
subjective and time-consuming. Thus, there is a need for objective methods to
assess surgical skills. Here, we use the Preferred Reporting Items for
Systematic Reviews and Meta-Analyses (PRISMA) guidelines to systematically
survey the literature on the use of Deep Neural Networks for automated and
objective surgical skill assessment, with a focus on kinematic data as putative
markers of surgical competency. There is considerable recent interest in deep
neural networks (DNN) due to the availability of powerful algorithms, multiple
datasets, some of which are publicly available, as well as efficient
computational hardware to train and host them. We have reviewed 530 papers, of
which we selected 25 for this systematic review. Based on this review, we
concluded that DNNs are powerful tools for automated, objective surgical skill
assessment using both kinematic and video data. The field would benefit from
large, publicly available, annotated datasets that are representative of the
surgical trainee and expert demographics and multimodal data beyond kinematics
and videos.
