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Deep learning computer vision algorithm for detecting kidney stone composition
dc.contributor.authorBlack, Kristian M.
dc.contributor.authorLaw, Hei
dc.contributor.authorAldoukhi, Ali
dc.contributor.authorDeng, Jia
dc.contributor.authorGhani, Khurshid R.
dc.date.accessioned2020-06-03T15:23:17Z
dc.date.availableWITHHELD_13_MONTHS
dc.date.available2020-06-03T15:23:17Z
dc.date.issued2020-06
dc.identifier.citationBlack, Kristian M.; Law, Hei; Aldoukhi, Ali; Deng, Jia; Ghani, Khurshid R. (2020). "Deep learning computer vision algorithm for detecting kidney stone composition." BJU International 125(6): 920-924.
dc.identifier.issn1464-4096
dc.identifier.issn1464-410X
dc.identifier.urihttps://hdl.handle.net/2027.42/155504
dc.publisherWiley Periodicals, Inc.
dc.subject.otherureteroscopy
dc.subject.otherholmium
dc.subject.otherlaser lithotripsy
dc.subject.other#KidneyStones
dc.subject.other#UroStone
dc.subject.otherdeep learning
dc.subject.otherartificial intelligence
dc.subject.othercomputer vision
dc.titleDeep learning computer vision algorithm for detecting kidney stone composition
dc.typeArticle
dc.rights.robotsIndexNoFollow
dc.subject.hlbsecondlevelInternal Medicine and Specialties
dc.subject.hlbtoplevelHealth Sciences
dc.description.peerreviewedPeer Reviewed
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/155504/1/bju15035_am.pdf
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/155504/2/bju15035.pdf
dc.identifier.doi10.1111/bju.15035
dc.identifier.sourceBJU International
dc.identifier.citedreferenceVassar GJ, Teichman JM, Glickman RD. Holmium:YAG lithotripsy efficiency varies with energy density. J Urol 1998; 160: 471 – 6
dc.identifier.citedreferenceOrdon M, Urbach D, Mamdani M, Saskin R, D’A Honey RJ, Pace KT. The surgical management of kidney stone disease: a population based time series analysis. J Urol 2014; 192: 1450 – 6
dc.identifier.citedreferenceDauw CA, Simeon L, Alruwaily AF et al. Contemporary practice patterns of flexible ureteroscopy for treating renal stones: results of a worldwide survey. J Endourol 2015; 29: 1221 – 30
dc.identifier.citedreferenceLaw H, Ghani K, Deng J. Surgeon Technical Skill Assessment using Computer Vision based Analysis. Proceedings of the 2nd Machine Learning for Healthcare Conference; 2017; Proceedings of Machine Learning Research.
dc.identifier.citedreferenceMiernik A, Eilers Y, Bolwien C et al. Automated analysis of urinary stone composition using Raman spectroscopy: pilot study for the development of a compact portable system for immediate postoperative ex vivo application. J Urol 2013; 190: 1895 – 900
dc.identifier.citedreferenceSchutz J, Miernik A, Brandenburg A, Schlager D. Experimental evaluation of human kidney stone spectra for intraoperative stone‐tissue‐instrument analysis using autofluorescence. J Urol 2019; 201: 182 – 7
dc.identifier.citedreferenceSerrat J, Lumbreras F, Blanco F, Valiente M, López‐Mesas M. myStone: A system for automatic kidney stone classification. Exp Syst Appl 2017; 89: 41 – 51
dc.identifier.citedreferenceHe K, Zhang X, Ren S, Sun J. Deep Residual Learning for Image Recognition. Paper presented at: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 27–30 June 2016, 2016
dc.identifier.citedreferenceGulshan V, Peng L, Coram M et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 2016; 316: 2402 – 10
dc.identifier.citedreferenceEsteva A, Kuprel B, Novoa RA et al. Dermatologist‐level classification of skin cancer with deep neural networks. Nature 2017; 542: 115 – 8
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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