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For: 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]
Number Citing Articles
1 Kirtac K, Aydin N, Lavanchy JL, Beldi G, Smit M, Woods MS, Aspart F. Surgical Phase Recognition: From Public Datasets to Real-World Data. Applied Sciences 2022;12:8746. [DOI: 10.3390/app12178746] [Reference Citation Analysis]
2 Sasaki K, Ito M, Kobayashi S, Kitaguchi D, Matsuzaki H, Kudo M, Hasegawa H, Takeshita N, Sugimoto M, Mitsunaga S, Gotohda N. Automated surgical workflow identification by artificial intelligence in laparoscopic hepatectomy: Experimental research. Int J Surg 2022;:106856. [PMID: 36031068 DOI: 10.1016/j.ijsu.2022.106856] [Reference Citation Analysis]
3 Kitaguchi D, Lee Y, Hayashi K, Nakajima K, Kojima S, Hasegawa H, Takeshita N, Mori K, Ito M. Development and Validation of a Model for Laparoscopic Colorectal Surgical Instrument Recognition Using Convolutional Neural Network-Based Instance Segmentation and Videos of Laparoscopic Procedures. JAMA Netw Open 2022;5:e2226265. [PMID: 35984660 DOI: 10.1001/jamanetworkopen.2022.26265] [Reference Citation Analysis]
4 Lam K, Abràmoff MD, Balibrea JM, Bishop SM, Brady RR, Callcut RA, Chand M, Collins JW, Diener MK, Eisenmann M, Fermont K, Neto MG, Hager GD, Hinchliffe RJ, Horgan A, Jannin P, Langerman A, Logishetty K, Mahadik A, Maier-Hein L, Antona EM, Mascagni P, Mathew RK, Müller-Stich BP, Neumuth T, Nickel F, Park A, Pellino G, Rudzicz F, Shah S, Slack M, Smith MJ, Soomro N, Speidel S, Stoyanov D, Tilney HS, Wagner M, Darzi A, Kinross JM, Purkayastha S. A Delphi consensus statement for digital surgery. NPJ Digit Med 2022;5:100. [PMID: 35854145 DOI: 10.1038/s41746-022-00641-6] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
5 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]
6 Amanian A, Heffernan A, Ishii M, Creighton FX, Thamboo A. The Evolution and Application of Artificial Intelligence in Rhinology: A State of the Art Review. Otolaryngol Head Neck Surg 2022;:1945998221110076. [PMID: 35787221 DOI: 10.1177/01945998221110076] [Reference Citation Analysis]
7 Tranter-entwistle I, Eglinton T, Hugh TJ, Connor S. Use of prospective video analysis to understand the impact of technical difficulty on operative process during laparoscopic cholecystectomy. HPB 2022. [DOI: 10.1016/j.hpb.2022.07.013] [Reference Citation Analysis]
8 Haidegger T, Speidel S, Stoyanov D, Satava RM. Robot-Assisted Minimally Invasive Surgery—Surgical Robotics in the Data Age. Proc IEEE 2022;110:835-46. [DOI: 10.1109/jproc.2022.3180350] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
9 Gumbs AA, Grasso V, Bourdel N, Croner R, Spolverato G, Frigerio I, Illanes A, Abu Hilal M, Park A, Elyan E. The Advances in Computer Vision That Are Enabling More Autonomous Actions in Surgery: A Systematic Review of the Literature. Sensors 2022;22:4918. [DOI: 10.3390/s22134918] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
10 Wang T, Xia J, Li R, Wang R, Stanojcic N, Li JO, Long E, Wang J, Zhang X, Li J, Wu X, Liu Z, Chen J, Chen H, Nie D, Ni H, Chen R, Chen W, Yin S, Lin D, Yan P, Xia Z, Lin S, Huang K, Lin H. Intelligent cataract surgery supervision and evaluation via deep learning. Int J Surg 2022;:106740. [PMID: 35760343 DOI: 10.1016/j.ijsu.2022.106740] [Reference Citation Analysis]
11 Moglia A, Georgiou K, Morelli L, Toutouzas K, Satava RM, Cuschieri A. Breaking down the silos of artificial intelligence in surgery: glossary of terms. Surg Endosc 2022. [PMID: 35729406 DOI: 10.1007/s00464-022-09371-y] [Reference Citation Analysis]
12 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]
13 Vedula SS, Ghazi A, Collins JW, Pugh C, Stefanidis D, Meireles O, Hung AJ, Schwaitzberg S, Levy JS, Sachdeva AK; and the Collaborative for Advanced Assessment of Robotic Surgical Skills. Artificial Intelligence Methods and Artificial Intelligence-Enabled Metrics for Surgical Education: A Multidisciplinary Consensus. J Am Coll Surg 2022;234:1181-92. [PMID: 35703817 DOI: 10.1097/XCS.0000000000000190] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
14 Berlet M, Vogel T, Ostler D, Czempiel T, Kähler M, Brunner S, Feussner H, Wilhelm D, Kranzfelder M. Surgical reporting for laparoscopic cholecystectomy based on phase annotation by a convolutional neural network (CNN) and the phenomenon of phase flickering: a proof of concept. Int J CARS. [DOI: 10.1007/s11548-022-02680-6] [Reference Citation Analysis]
15 Morris MX, Rajesh A, Asaad M, Hassan A, Saadoun R, Butler CE. Deep Learning Applications in Surgery: Current Uses and Future Directions. Am Surg 2022;:31348221101490. [PMID: 35567312 DOI: 10.1177/00031348221101490] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
16 Sanchez-Matilla R, Robu M, Grammatikopoulou M, Luengo I, Stoyanov D. Data-centric multi-task surgical phase estimation with sparse scene segmentation. Int J Comput Assist Radiol Surg 2022. [PMID: 35505149 DOI: 10.1007/s11548-022-02616-0] [Reference Citation Analysis]
17 Das A, Bano S, Vasconcelos F, Khan DZ, Marcus HJ, Stoyanov D. Reducing Prediction volatility in the surgical workflow recognition of endoscopic pituitary surgery. Int J Comput Assist Radiol Surg 2022. [PMID: 35362848 DOI: 10.1007/s11548-022-02599-y] [Reference Citation Analysis]
18 Lam K, Chen J, Wang Z, Iqbal FM, Darzi A, Lo B, Purkayastha S, Kinross JM. Machine learning for technical skill assessment in surgery: a systematic review. NPJ Digit Med 2022;5:24. [PMID: 35241760 DOI: 10.1038/s41746-022-00566-0] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
19 Jumah F, Raju B, Nagaraj A, Shinde R, Lescott C, Sun H, Gupta G, Nanda A. Uncharted Waters of Machine and Deep Learning for Surgical Phase Recognition in Neurosurgery. World Neurosurg 2022;160:4-12. [PMID: 35026457 DOI: 10.1016/j.wneu.2022.01.020] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
20 Yeh HH, Jain AM, Fox O, Wang SY. PhacoTrainer: A Multicenter Study of Deep Learning for Activity Recognition in Cataract Surgical Videos. Transl Vis Sci Technol 2021;10:23. [PMID: 34784415 DOI: 10.1167/tvst.10.13.23] [Reference Citation Analysis]
21 Unberath M, Gao C, Hu Y, Judish M, Taylor RH, Armand M, Grupp R. The Impact of Machine Learning on 2D/3D Registration for Image-Guided Interventions: A Systematic Review and Perspective. Front Robot AI 2021;8:716007. [PMID: 34527706 DOI: 10.3389/frobt.2021.716007] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
22 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]
23 Rivas-blanco I, Perez-del-pulgar CJ, Garcia-morales I, Munoz VF. A Review on Deep Learning in Minimally Invasive Surgery. IEEE Access 2021;9:48658-78. [DOI: 10.1109/access.2021.3068852] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]