BPG is committed to discovery and dissemination of knowledge
Cited by in F6Publishing
For: Kowalewski K, Garrow CR, Schmidt MW, Benner L, Müller-stich BP, Nickel F. Sensor-based machine learning for workflow detection and as key to detect expert level in laparoscopic suturing and knot-tying. Surg Endosc 2019;33:3732-40. [DOI: 10.1007/s00464-019-06667-4] [Cited by in Crossref: 10] [Cited by in F6Publishing: 16] [Article Influence: 3.3] [Reference Citation Analysis]
Number Citing Articles
1 Pooransari P, Mehrabi S, Mirzamoradi M, Salehgargari S, Afrakhteh M. Comparison of Parameters of Fetal Doppler Echocardiography Between Mothers with and Without Diabetes. Int J Endocrinol Metab 2022;20. [DOI: 10.5812/ijem-117524] [Reference Citation Analysis]
2 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]
3 Yanik E, Intes X, Kruger U, Yan P, Diller D, Van Voorst B, Makled B, Norfleet J, De S. Deep neural networks for the assessment of surgical skills: A systematic review. Journal of Defense Modeling & Simulation 2022;19:159-71. [DOI: 10.1177/15485129211034586] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
4 Fuchs R, Van Praet KM, Bieck R, Kempfert J, Holzhey D, Kofler M, Borger MA, Jacobs S, Falk V, Neumuth T. A system for real-time multivariate feature combination of endoscopic mitral valve simulator training data. Int J Comput Assist Radiol Surg 2022. [PMID: 35294716 DOI: 10.1007/s11548-022-02588-1] [Reference Citation Analysis]
5 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 F6Publishing: 1] [Reference Citation Analysis]
6 Stenmark M, Omerbašić E, Magnusson M, Andersson V, Abrahamsson M, Tran PK. Vision-based Tracking of Surgical Motion during Live Open-Heart Surgery. J Surg Res 2021;271:106-16. [PMID: 34879315 DOI: 10.1016/j.jss.2021.10.025] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
7 Bilgic E, Gorgy A, Yang A, Cwintal M, Ranjbar H, Kahla K, Reddy D, Li K, Ozturk H, Zimmermann E, Quaiattini A, Abbasgholizadeh-Rahimi S, Poenaru D, Harley JM. Exploring the roles of artificial intelligence in surgical education: A scoping review. Am J Surg 2021:S0002-9610(21)00682-6. [PMID: 34865736 DOI: 10.1016/j.amjsurg.2021.11.023] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
8 Bamba Y, Ogawa S, Itabashi M, Kameoka S, Okamoto T, Yamamoto M. Automated recognition of objects and types of forceps in surgical images using deep learning. Sci Rep 2021;11:22571. [PMID: 34799625 DOI: 10.1038/s41598-021-01911-1] [Reference Citation Analysis]
9 Romero P, Carstensen L, Kössler‐ebs J, Wennberg E, Müller‐stich BP, Nickel F, Günther P. Learning and application of intracorporal slipping knot techniques in minimally invasive surgery. Surgical Practice 2021;25:218-22. [DOI: 10.1111/1744-1633.12534] [Reference Citation Analysis]
10 Kirubarajan A, Young D, Khan S, Crasto N, Sobel M, Sussman D. Artificial Intelligence and Surgical Education: A Systematic Scoping Review of Interventions. J Surg Educ 2021:S1931-7204(21)00258-0. [PMID: 34756807 DOI: 10.1016/j.jsurg.2021.09.012] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
11 Alnafisee N, Zafar S, Vedula SS, Sikder S. Current methods for assessing technical skill in cataract surgery. J Cataract Refract Surg 2021;47:256-64. [PMID: 32675650 DOI: 10.1097/j.jcrs.0000000000000322] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
12 Garrow CR, Kowalewski KF, Li L, Wagner M, Schmidt MW, Engelhardt S, Hashimoto DA, Kenngott HG, Bodenstedt S, Speidel S, Müller-Stich BP, Nickel F. Machine Learning for Surgical Phase Recognition: A Systematic Review. Ann Surg 2021;273:684-93. [PMID: 33201088 DOI: 10.1097/SLA.0000000000004425] [Cited by in Crossref: 8] [Cited by in F6Publishing: 26] [Article Influence: 8.0] [Reference Citation Analysis]
13 Willuth E, Hardon SF, Lang F, Haney CM, Felinska EA, Kowalewski KF, Müller-Stich BP, Horeman T, Nickel F. Robotic-assisted cholecystectomy is superior to laparoscopic cholecystectomy in the initial training for surgical novices in an ex vivo porcine model: a randomized crossover study. Surg Endosc 2021. [PMID: 33638104 DOI: 10.1007/s00464-021-08373-6] [Cited by in F6Publishing: 3] [Reference Citation Analysis]
14 Castillo-Segura P, Fernández-Panadero C, Alario-Hoyos C, Muñoz-Merino PJ, Delgado Kloos C. Objective and automated assessment of surgical technical skills with IoT systems: A systematic literature review. Artif Intell Med 2021;112:102007. [PMID: 33581827 DOI: 10.1016/j.artmed.2020.102007] [Cited by in F6Publishing: 4] [Reference Citation Analysis]
15 Pastewski J, Baker D, Somerset A, Leonard K, Azzie G, Roach VA, Ziegler K, Brahmamdam P. Analysis of Instrument Motion and the Impact of Residency Level and Concurrent Distraction on Laparoscopic Skills. J Surg Educ 2021;78:265-74. [PMID: 32741690 DOI: 10.1016/j.jsurg.2020.07.012] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
16 Ganni S, Botden SMBI, Chmarra M, Li M, Goossens RHM, Jakimowicz JJ. Validation of Motion Tracking Software for Evaluation of Surgical Performance in Laparoscopic Cholecystectomy. J Med Syst 2020;44:56. [PMID: 31980955 DOI: 10.1007/s10916-020-1525-9] [Cited by in F6Publishing: 4] [Reference Citation Analysis]