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Cited by in F6Publishing
For: Kenngott HG, Wünscher JJ, Wagner M, Preukschas A, Wekerle AL, Neher P, Suwelack S, Speidel S, Nickel F, Oladokun D, Albala L, Maier-Hein L, Dillmann R, Meinzer HP, Müller-Stich BP. OpenHELP (Heidelberg laparoscopy phantom): development of an open-source surgical evaluation and training tool. Surg Endosc 2015;29:3338-47. [PMID: 25673345 DOI: 10.1007/s00464-015-4094-0] [Cited by in Crossref: 15] [Cited by in F6Publishing: 9] [Article Influence: 2.1] [Reference Citation Analysis]
Number Citing Articles
1 Kenngott HG, Preukschas AA, Wagner M, Nickel F, Müller M, Bellemann N, Stock C, Fangerau M, Radeleff B, Kauczor HU, Meinzer HP, Maier-Hein L, Müller-Stich BP. Mobile, real-time, and point-of-care augmented reality is robust, accurate, and feasible: a prospective pilot study. Surg Endosc 2018;32:2958-67. [PMID: 29602988 DOI: 10.1007/s00464-018-6151-y] [Cited by in Crossref: 5] [Cited by in F6Publishing: 1] [Article Influence: 1.3] [Reference Citation Analysis]
2 Haney CM, Karadza E, Limen EF, de Santibanes M, Kinny-köster B, Müller PC, Bintintan VV, Kulu Y, Hackert T, Müller-stich B, Nickel F. Training and learning curves in minimally invasive pancreatic surgery: from simulation to mastery. Journal of Pancreatology 2020;3:101-10. [DOI: 10.1097/jp9.0000000000000050] [Cited by in Crossref: 6] [Article Influence: 3.0] [Reference Citation Analysis]
3 Kowalewski K, Hendrie JD, Schmidt MW, Garrow CR, Bruckner T, Proctor T, Paul S, Adigüzel D, Bodenstedt S, Erben A, Kenngott H, Erben Y, Speidel S, Müller-stich BP, Nickel F. Development and validation of a sensor- and expert model-based training system for laparoscopic surgery: the iSurgeon. Surg Endosc 2017;31:2155-65. [DOI: 10.1007/s00464-016-5213-2] [Cited by in Crossref: 32] [Cited by in F6Publishing: 18] [Article Influence: 5.3] [Reference Citation Analysis]
4 Pietrabissa A, Marconi S, Negrello E, Mauri V, Peri A, Pugliese L, Marone EM, Auricchio F. An overview on 3D printing for abdominal surgery. Surg Endosc 2020;34:1-13. [DOI: 10.1007/s00464-019-07155-5] [Cited by in Crossref: 11] [Cited by in F6Publishing: 9] [Article Influence: 3.7] [Reference Citation Analysis]
5 Engelhardt S, De Simone R, Full PM, Karck M, Wolf I. Improving Surgical Training Phantoms by Hyperrealism: Deep Unpaired Image-to-Image Translation from Real Surgeries. In: Frangi AF, Schnabel JA, Davatzikos C, Alberola-lópez C, Fichtinger G, editors. Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. Cham: Springer International Publishing; 2018. pp. 747-55. [DOI: 10.1007/978-3-030-00928-1_84] [Cited by in Crossref: 11] [Cited by in F6Publishing: 2] [Article Influence: 2.8] [Reference Citation Analysis]
6 Jose RR, Rodriguez MJ, Dixon TA, Omenetto F, Kaplan DL. Evolution of Bioinks and Additive Manufacturing Technologies for 3D Bioprinting. ACS Biomater Sci Eng 2016;2:1662-78. [DOI: 10.1021/acsbiomaterials.6b00088] [Cited by in Crossref: 167] [Cited by in F6Publishing: 93] [Article Influence: 27.8] [Reference Citation Analysis]
7 Pfeiffer M, Riediger C, Weitz J, Speidel S. Learning soft tissue behavior of organs for surgical navigation with convolutional neural networks.Int J Comput Assist Radiol Surg. 2019;14:1147-1155. [PMID: 30993520 DOI: 10.1007/s11548-019-01965-7] [Cited by in Crossref: 21] [Cited by in F6Publishing: 12] [Article Influence: 7.0] [Reference Citation Analysis]
8 Engelhardt S, Sauerzapf S, Preim B, Karck M, Wolf I, De Simone R. Flexible and comprehensive patient-specific mitral valve silicone models with chordae tendineae made from 3D-printable molds. Int J Comput Assist Radiol Surg 2019;14:1177-86. [PMID: 30997636 DOI: 10.1007/s11548-019-01971-9] [Cited by in Crossref: 13] [Cited by in F6Publishing: 13] [Article Influence: 4.3] [Reference Citation Analysis]
9 Yeung C, Lam K, Cheung JL, Tjokronegoro A, Law CSJ, Singh S, Foo C, Sreedhar B, Hoa XD. Overcoming Abdominal and Pelvic Cavity Workspace Constraints in Robotic-Assisted NOTES. Journal of Robotics 2020;2020:1-8. [DOI: 10.1155/2020/8590539] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
10 Wagner M, Bihlmaier A, Kenngott HG, Mietkowski P, Scheikl PM, Bodenstedt S, Schiepe-Tiska A, Vetter J, Nickel F, Speidel S, Wörn H, Mathis-Ullrich F, Müller-Stich BP. A learning robot for cognitive camera control in minimally invasive surgery. Surg Endosc 2021;35:5365-74. [PMID: 33904989 DOI: 10.1007/s00464-021-08509-8] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
11 Ryska O, Serclova Z, Martinek J, Dolezel R, Kalvach J, Juhas S, Juhasova J, Bunganic B, Laszikova E, Ryska M. A new experimental model of calculous cholecystitis suitable for the evaluation and training of minimally invasive approaches to cholecystectomy. Surg Endosc 2017;31:987-94. [PMID: 27495340 DOI: 10.1007/s00464-016-5061-0] [Reference Citation Analysis]