BPG is committed to discovery and dissemination of knowledge
Cited by in F6Publishing
For: 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: 27] [Cited by in F6Publishing: 28] [Article Influence: 6.8] [Reference Citation Analysis]
Number Citing Articles
1 Guzmán-garcía C, Sánchez-gonzález P, Oropesa I, Gómez EJ. Automatic Assessment of Procedural Skills Based on the Surgical Workflow Analysis Derived from Speech and Video. Bioengineering 2022;9:753. [DOI: 10.3390/bioengineering9120753] [Reference Citation Analysis]
2 Neumann J, Uciteli A, Meschke T, Bieck R, Franke S, Herre H, Neumuth T. Ontology-based surgical workflow recognition and prediction. Journal of Biomedical Informatics 2022. [DOI: 10.1016/j.jbi.2022.104240] [Reference Citation Analysis]
3 Rodrigues M, Mayo M, Patros P. Surgical Tool Datasets for Machine Learning Research: A Survey. Int J Comput Vis. [DOI: 10.1007/s11263-022-01640-6] [Reference Citation Analysis]
4 Fiorini P, Goldberg KY, Liu Y, Taylor RH. Concepts and Trends in Autonomy for Robot-Assisted Surgery. Proc IEEE 2022;110:993-1011. [DOI: 10.1109/jproc.2022.3176828] [Reference Citation Analysis]
5 Kowalewski K, Egen L, Fischetti CE, Puliatti S, Juan GR, Taratkin M, Ines RB, Sidoti Abate MA, Mühlbauer J, Wessels F, Checcucci E, Cacciamani G. Artificial intelligence for renal cancer: From imaging to histology and beyond. Asian Journal of Urology 2022. [DOI: 10.1016/j.ajur.2022.05.003] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
6 Puliatti S, Eissa A, Checcucci E, Piazza P, Amato M, Scarcella S, Rivas JG, Taratkin M, Marenco J, Rivero IB, Kowalewski K, Cacciamani G, El-sherbiny A, Zoeir A, El-bahnasy AM, De Groote R, Mottrie A, Micali S. New imaging technologies for robotic kidney cancer surgery. Asian Journal of Urology 2022. [DOI: 10.1016/j.ajur.2022.03.008] [Reference Citation Analysis]
7 Junger D, Frommer SM, Burgert O. State-of-the-art of situation recognition systems for intraoperative procedures. Med Biol Eng Comput 2022. [PMID: 35178622 DOI: 10.1007/s11517-022-02520-4] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
8 Li L, Feng P, Ding H, Wang G. A Preliminary Exploration to Make Stereotactic Surgery Robots Aware of the Semantic 2D/3D Working Scene. IEEE Trans Med Robot Bionics 2022;4:17-27. [DOI: 10.1109/tmrb.2021.3124160] [Reference Citation Analysis]
9 De Momi E. Historical Perspectives. Robotics in Neurosurgery 2022. [DOI: 10.1007/978-3-031-08380-8_2] [Reference Citation Analysis]
10 Moccia S, De Momi E. AIM in Medical Robotics. Artificial Intelligence in Medicine 2022. [DOI: 10.1007/978-3-030-64573-1_64] [Reference Citation Analysis]
11 Wang Z, Lu B, Long Y, Zhong F, Cheung T, Dou Q, Liu Y. AutoLaparo: A New Dataset of Integrated Multi-tasks for Image-guided Surgical Automation in Laparoscopic Hysterectomy. Lecture Notes in Computer Science 2022. [DOI: 10.1007/978-3-031-16449-1_46] [Reference Citation Analysis]
12 Meli D, Tagliabue E, Dall'alba D, Fiorini P. Autonomous tissue retraction with a biomechanically informed logic based framework. 2021 International Symposium on Medical Robotics (ISMR) 2021. [DOI: 10.1109/ismr48346.2021.9661573] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
13 Moglia A, Georgiou K, Georgiou E, Satava RM, Cuschieri A. A systematic review on artificial intelligence in robot-assisted surgery. Int J Surg 2021;95:106151. [PMID: 34695601 DOI: 10.1016/j.ijsu.2021.106151] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 7.0] [Reference Citation Analysis]
14 Zhang B, Ghanem A, Simes A, Choi H, Yoo A. Surgical workflow recognition with 3DCNN for Sleeve Gastrectomy. Int J Comput Assist Radiol Surg 2021;16:2029-36. [PMID: 34415503 DOI: 10.1007/s11548-021-02473-3] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
15 Combi C, Galetto F, Nakawala HC, Pozzi G, Zerbato F. Enriching surgical process models by BPMN extensions for temporal durations. Proceedings of the 36th Annual ACM Symposium on Applied Computing 2021. [DOI: 10.1145/3412841.3441939] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
16 Guzmán-García C, Gómez-Tome M, Sánchez-González P, Oropesa I, Gómez EJ. Speech-Based Surgical Phase Recognition for Non-Intrusive Surgical Skills' Assessment in Educational Contexts. Sensors (Basel) 2021;21:1330. [PMID: 33668544 DOI: 10.3390/s21041330] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 5.0] [Reference Citation Analysis]
17 Moccia S, De Momi E. AIM in Medical Robotics. Artificial Intelligence in Medicine 2021. [DOI: 10.1007/978-3-030-58080-3_64-1] [Reference Citation Analysis]
18 Bieck R, Heuermann K, Pirlich M, Neumann J, Neumuth T. Language-based translation and prediction of surgical navigation steps for endoscopic wayfinding assistance in minimally invasive surgery. Int J Comput Assist Radiol Surg 2020;15:2089-100. [PMID: 33037490 DOI: 10.1007/s11548-020-02264-2] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
19 Tanzi L, Piazzolla P, Vezzetti E. Intraoperative surgery room management: A deep learning perspective. Int J Med Robot 2020;16:1-12. [PMID: 32510857 DOI: 10.1002/rcs.2136] [Cited by in Crossref: 9] [Cited by in F6Publishing: 7] [Article Influence: 4.5] [Reference Citation Analysis]
20 Kitaguchi D, Takeshita N, Matsuzaki H, Oda T, Watanabe M, Mori K, Kobayashi E, Ito M. Automated laparoscopic colorectal surgery workflow recognition using artificial intelligence: Experimental research. Int J Surg. 2020;79:88-94. [PMID: 32413503 DOI: 10.1016/j.ijsu.2020.05.015] [Cited by in Crossref: 38] [Cited by in F6Publishing: 26] [Article Influence: 19.0] [Reference Citation Analysis]
21 Nakawala H, De Momi E, Bianchi R, Catellani M, De Cobelli O, Jannin P, Ferrigno G, Fiorini P. Toward a Neural-Symbolic Framework for Automated Workflow Analysis in Surgery. IFMBE Proceedings 2020. [DOI: 10.1007/978-3-030-31635-8_192] [Reference Citation Analysis]
22 Shi P, Zhao Z, Hu S, Chang F. Real-Time Surgical Tool Detection in Minimally Invasive Surgery Based on Attention-Guided Convolutional Neural Network. IEEE Access 2020;8:228853-228862. [DOI: 10.1109/access.2020.3046258] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
23 Takeshita N, Mori K, Ito M. Constructing Database of Tacit Knowledge and Its Application for Surgical Device Innovation in Laparoscopic Surgery. JJSCAS 2020;22:40-43. [DOI: 10.5759/jscas.22.40] [Reference Citation Analysis]
24 Jin Y, Li H, Dou Q, Chen H, Qin J, Fu CW, Heng PA. Multi-task recurrent convolutional network with correlation loss for surgical video analysis. Med Image Anal 2020;59:101572. [PMID: 31639622 DOI: 10.1016/j.media.2019.101572] [Cited by in Crossref: 65] [Cited by in F6Publishing: 67] [Article Influence: 21.7] [Reference Citation Analysis]
25 Tapp A, Blatt JE, St-clair HS, Audette MA. Towards a “Surgical GPS”: Combining Surgical Ontologies with Physician-Designated Anatomical Landmarks. VipIMAGE 2019 2019. [DOI: 10.1007/978-3-030-32040-9_56] [Cited by in Crossref: 1] [Article Influence: 0.3] [Reference Citation Analysis]