Deep learning for Evaluation and Prediction of TecHnical Skills in robotic-assisted vaginal cuff closure study.

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Bibliographic Details
Title: Deep learning for Evaluation and Prediction of TecHnical Skills in robotic-assisted vaginal cuff closure study.
Authors: Tesfai F; Institute for Women's Health, University College London, London, England; The Griffin Institute, Harrow, England., Xu J; Department of Medical Physics and Biomedical Engineering, University College London, London, England., Anastasiou D; Department of Medical Physics and Biomedical Engineering, University College London, London, England., He R; Department of Medical Physics and Biomedical Engineering, University College London, London, England., Boal M; The Griffin Institute, Harrow, England; Division of Surgery and Interventional Science, XXX, University College London, London, England., Aranan Y; Department of General Surgery, The Royal London Hospital, Barts Health NHS Trust, London, England., Lingam G; Institute for Women's Health, University College London, London, England., Shah D; University College London Medical School, London, England., Stoyanov D; Department of Medical Physics and Biomedical Engineering, University College London, London, England., Chandrasekaran D; University College London Hospitals NHS Foundation Trust., Mazomenos E; Department of Medical Physics and Biomedical Engineering, University College London, London, England., Francis N; The Griffin Institute, Harrow, England; Division of Surgery and Interventional Science, XXX, University College London, London, England. Electronic address: n.francis@griffininstitute.org.uk.
Source: American journal of obstetrics and gynecology [Am J Obstet Gynecol] 2026 Aug; Vol. 235 (2), pp. 458-467. Date of Electronic Publication: 2026 Mar 19.
Publication Type: Journal Article; Multicenter Study; Observational Study
Journal Info: Publisher: Elsevier Country of Publication: United States NLM ID: 0370476 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1097-6868 (Electronic) Linking ISSN: 00029378 NLM ISO Abbreviation: Am J Obstet Gynecol Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1097-6868
DOI:10.1016/j.ajog.2026.03.015