Automated assessment of simulated laparoscopic surgical skill performance using deep learning

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dc.contributor.authorPower, Daviden
dc.contributor.authorBurke, Cathyen
dc.contributor.authorMadden, Michael G.en
dc.contributor.authorUllah, Ihsanen
dc.contributor.funderResearch Irelanden
dc.date.accessioned2025-05-13T08:54:24Z
dc.date.available2025-05-13T08:54:24Z
dc.date.issued2025en
dc.description.abstractArtificial intelligence (AI) has the potential to improve healthcare and patient safety and is currently being adopted across various fields of medicine and healthcare. AI and in particular computer vision (CV) are well suited to the analysis of minimally invasive surgical simulation videos for training and performance improvement. CV techniques have rapidly improved in recent years from accurately recognizing objects, instruments, and gestures to phases of surgery and more recently to remembering past surgical steps. Lack of labeled data is a particular problem in surgery considering its complexity, as human annotation and manual assessment are both expensive in time and cost, and in most cases rely on direct intervention of clinical expertise. In this study, we introduce a newly collected simulated Laparoscopic Surgical Performance Dataset (LSPD) specifically designed to address these challenges. Unlike existing datasets that focus on instrument tracking or anatomical structure recognition, the LSPD is tailored for evaluating simulated laparoscopic surgical skill performance at various expertise levels. We provide detailed statistical analyses to identify and compare poorly performed and well-executed operations across different skill levels (novice, trainee, expert) for three specific skills: stack, bands, and tower. We employ a 3-dimensional convolutional neural network (3DCNN) with a weakly-supervised approach to classify the experience levels of surgeons. Our results show that the 3DCNN effectively distinguishes between novices, trainees, and experts, achieving an F1 score of 0.91 and an AUC of 0.92. This study highlights the value of the LSPD dataset and demonstrates the potential of leveraging 3DCNN-based and weakly-supervised approaches to automate the evaluation of surgical performance, reducing reliance on manual expert annotation and assessments. These advancements contribute to improving surgical training and performance analysis.en
dc.description.sponsorshipResearch Ireland (Grant number SFI/12/RC/2289_P2)en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.articleid13591en
dc.identifier.citationPower, D., Burke, C., Madden, M. G. and Ullah, I. (2025) 'Automated assessment of simulated laparoscopic surgical skill performance using deep learning', Scientific Reports, 15(1), 13591 (16pp). https://doi.org/10.1038/s41598-025-96336-5en
dc.identifier.doi10.1038/s41598-025-96336-5en
dc.identifier.issn20452322en
dc.identifier.issued1
dc.identifier.journaltitleScientific Reportsen
dc.identifier.urihttps://hdl.handle.net/10468/17450
dc.identifier.volume15
dc.language.isoenen
dc.publisherNature Researchen
dc.relation.projectSFI/12/RC/2289_P2en
dc.rights© 2025, the Author(s). 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence 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. en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subject3DCNNen
dc.subjectAutomated assessmenten
dc.subjectDeep learningen
dc.subjectLaparoscopic surgeryen
dc.titleAutomated assessment of simulated laparoscopic surgical skill performance using deep learningen
dc.typeArticle (peer reviewed)en
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