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Cited by in F6Publishing
For: Gibaud B, Forestier G, Feldmann C, Ferrigno G, Gonçalves P, Haidegger T, Julliard C, Katić D, Kenngott H, Maier-Hein L, März K, de Momi E, Nagy DÁ, Nakawala H, Neumann J, Neumuth T, Rojas Balderrama J, Speidel S, Wagner M, Jannin P. Toward a standard ontology of surgical process models. Int J Comput Assist Radiol Surg 2018;13:1397-408. [PMID: 30006820 DOI: 10.1007/s11548-018-1824-5] [Cited by in Crossref: 19] [Cited by in F6Publishing: 15] [Article Influence: 4.8] [Reference Citation Analysis]
Number Citing Articles
1 Huaulmé A, Dardenne G, Labbe B, Gelin M, Chesneau C, Diverrez JM, Riffaud L, Jannin P. Surgical declarative knowledge learning: concept and acceptability study. Comput Assist Surg (Abingdon) 2022;27:74-83. [PMID: 35727207 DOI: 10.1080/24699322.2022.2086484] [Reference Citation Analysis]
2 Carrillo F, Esfandiari H, Müller S, von Atzigen M, Massalimova A, Suter D, Laux CJ, Spirig JM, Farshad M, Fürnstahl P. Surgical Process Modeling for Open Spinal Surgeries. Front Surg 2022;8:776945. [DOI: 10.3389/fsurg.2021.776945] [Reference Citation Analysis]
3 Maier-Hein L, Eisenmann M, Sarikaya D, März K, Collins T, Malpani A, Fallert J, Feussner H, Giannarou S, Mascagni P, Nakawala H, Park A, Pugh C, Stoyanov D, Vedula SS, Cleary K, Fichtinger G, Forestier G, Gibaud B, Grantcharov T, Hashizume M, Heckmann-Nötzel D, Kenngott HG, Kikinis R, Mündermann L, Navab N, Onogur S, Roß T, Sznitman R, Taylor RH, Tizabi MD, Wagner M, Hager GD, Neumuth T, Padoy N, Collins J, Gockel I, Goedeke J, Hashimoto DA, Joyeux L, Lam K, Leff DR, Madani A, Marcus HJ, Meireles O, Seitel A, Teber D, Ückert F, Müller-Stich BP, Jannin P, Speidel S. Surgical data science - from concepts toward clinical translation. Med Image Anal 2021;76:102306. [PMID: 34879287 DOI: 10.1016/j.media.2021.102306] [Cited by in Crossref: 14] [Cited by in F6Publishing: 7] [Article Influence: 14.0] [Reference Citation Analysis]
4 Qin Y, Allan M, Burdick JW, Azizian M. Autonomous Hierarchical Surgical State Estimation During Robot-Assisted Surgery Through Deep Neural Networks. IEEE Robot Autom Lett 2021;6:6220-7. [DOI: 10.1109/lra.2021.3091728] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
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6 Klodmann J, Schlenk C, Hellings-kuß A, Bahls T, Unterhinninghofen R, Albu-schäffer A, Hirzinger G. An Introduction to Robotically Assisted Surgical Systems: Current Developments and Focus Areas of Research. Curr Robot Rep 2021;2:321-32. [DOI: 10.1007/s43154-021-00064-3] [Cited by in Crossref: 5] [Cited by in F6Publishing: 1] [Article Influence: 5.0] [Reference Citation Analysis]
7 Mohamad UH, Ahmad MN, Benferdia Y, Shapi'i A, Bajuri MY. An Overview of Ontologies in Virtual Reality-Based Training for Healthcare Domain. Front Med (Lausanne) 2021;8:698855. [PMID: 34307424 DOI: 10.3389/fmed.2021.698855] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
8 Ward TM, Fer DM, Ban Y, Rosman G, Meireles OR, Hashimoto DA. Challenges in surgical video annotation. Comput Assist Surg (Abingdon) 2021;26:58-68. [PMID: 34126014 DOI: 10.1080/24699322.2021.1937320] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
9 Manzoor S, Rocha YG, Joo S, Bae S, Kim E, Joo K, Kuc T. Ontology-Based Knowledge Representation in Robotic Systems: A Survey Oriented toward Applications. Applied Sciences 2021;11:4324. [DOI: 10.3390/app11104324] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 5.0] [Reference Citation Analysis]
10 Nagyné Elek R, Haidegger T. Non-Technical Skill Assessment and Mental Load Evaluation in Robot-Assisted Minimally Invasive Surgery. Sensors (Basel) 2021;21:2666. [PMID: 33920087 DOI: 10.3390/s21082666] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
11 [DOI: 10.1109/icar46387.2019.8981619] [Cited by in Crossref: 6] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
12 Vercauteren T, Unberath M, Padoy N, Navab N. CAI4CAI: The Rise of Contextual Artificial Intelligence in Computer Assisted Interventions. Proc IEEE Inst Electr Electron Eng. 2020;108:198-214. [PMID: 31920208 DOI: 10.1109/jproc.2019.2946993] [Cited by in Crossref: 32] [Cited by in F6Publishing: 30] [Article Influence: 10.7] [Reference Citation Analysis]
13 Bracq MS, Michinov E, Arnaldi B, Caillaud B, Gibaud B, Gouranton V, Jannin P. Learning procedural skills with a virtual reality simulator: An acceptability study. Nurse Educ Today 2019;79:153-60. [PMID: 31132727 DOI: 10.1016/j.nedt.2019.05.026] [Cited by in Crossref: 25] [Cited by in F6Publishing: 9] [Article Influence: 8.3] [Reference Citation Analysis]
14 Gholinejad M, J Loeve A, Dankelman J. Surgical process modelling strategies: which method to choose for determining workflow? Minim Invasive Ther Allied Technol 2019;28:91-104. [PMID: 30915885 DOI: 10.1080/13645706.2019.1591457] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
15 Nakawala H, Bianchi R, Pescatori LE, De Cobelli O, Ferrigno G, De Momi E. “Deep-Onto” network for surgical workflow and context recognition. Int J CARS 2019;14:685-96. [DOI: 10.1007/s11548-018-1882-8] [Cited by in Crossref: 14] [Cited by in F6Publishing: 12] [Article Influence: 3.5] [Reference Citation Analysis]