Deep neural networks for the assessment of surgical skills: A systematic review

Erim Yanik, Xavier Intes, Uwe Kruger, Pingkun Yan, David Diller, Brian Van Voorst, Basiel Makled, Jack Norfleet and Suvranu De

The Journal of Defense Modeling and Simulation, 2022, vol. 19, issue 2, 159-171

Abstract: 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 (DNNs) 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 potent tools for automated, objective surgical skill assessment using both kinematic and video data. The field would benefit from large, publicly available, annotated datasets representing the surgical trainee and expert demographics and multimodal data beyond kinematics and videos.

Keywords: Deep learning; deep neural network; artificial intelligence; convolutional neural network; LSTM; GRU; RNN; surgical skill assessment; laparoscopic surgery; robotic surgery; virtual surgical simulators (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/15485129211034586 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:joudef:v:19:y:2022:i:2:p:159-171

DOI: 10.1177/15485129211034586

Access Statistics for this article

More articles in The Journal of Defense Modeling and Simulation
Bibliographic data for series maintained by SAGE Publications ().