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. 2023 Mar 30;7(6):780–796. doi: 10.1038/s41551-023-01010-8

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© The Author(s) 2023

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 4 — a,b, We train SAIS on video samples exclusively from USC to decode the skill-level of needle handling (a) and needle driving (b), and deploy it on video samples from USC, SAH and HMH. Results are an average (±1 standard deviation) of ten Monte Carlo cross-validation steps. c,d, We also present the attention placed on frames by SAIS for a video sample of low-skill needle handling (c) and needle driving (d). Images with an orange bounding box indicate that SAIS places the highest attention on frames depicting visual states consistent with the respective skill assessment criteria. These criteria correspond to needle repositions and needle adjustments, respectively. e, Surgical skills profile depicting the skill assessment of needle handling and needle driving from a single surgical case at SAH. f,g, Ratio of low-skill needle handling (f) and needle driving (g) in each of the 30 surgical cases at SAH. The horizontal dashed lines represent the average ratio of low-skill activity at USC.