Video-based formative and summative assessment of surgical tasks using deep learning.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
19 01 2023
19 01 2023
Historique:
received:
11
05
2022
accepted:
13
12
2022
entrez:
19
1
2023
pubmed:
20
1
2023
medline:
24
1
2023
Statut:
epublish
Résumé
To ensure satisfactory clinical outcomes, surgical skill assessment must be objective, time-efficient, and preferentially automated-none of which is currently achievable. Video-based assessment (VBA) is being deployed in intraoperative and simulation settings to evaluate technical skill execution. However, VBA is manual, time-intensive, and prone to subjective interpretation and poor inter-rater reliability. Herein, we propose a deep learning (DL) model that can automatically and objectively provide a high-stakes summative assessment of surgical skill execution based on video feeds and low-stakes formative assessment to guide surgical skill acquisition. Formative assessment is generated using heatmaps of visual features that correlate with surgical performance. Hence, the DL model paves the way for the quantitative and reproducible evaluation of surgical tasks from videos with the potential for broad dissemination in surgical training, certification, and credentialing.
Identifiants
pubmed: 36658186
doi: 10.1038/s41598-022-26367-9
pii: 10.1038/s41598-022-26367-9
pmc: PMC9852463
doi:
Types de publication
Journal Article
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
1038Informations de copyright
© 2023. The Author(s).
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