Artificial intelligence in functional urology: how it may shape the future.

作者: Zine-Eddine Khene , Rose Khavari , Imad Bentellis , Benoit Peyronnet , Sonia Guérin

DOI: 10.1097/MOU.0000000000000888

关键词:

摘要: Purpose of review The aim the present manuscript is to provide an overview on current state artificial intelligence (AI) tools in either decision making, diagnosis, treatment options, or outcome prediction functional urology. Recent findings Several recent studies have shed light promising potential AI urology investigate lower urinary tract dysfunction pathophysiology but also as a diagnostic tool by enhancing existing evaluations such dynamic magnetic resonance imaging urodynamics. may improve surgical education and training because its automated performance metrics recording. By bringing models, strong therapeutic implications field near future. be implemented innovative devices e-bladder diary electromechanical sphincter could facilitate development remote medicine. Summary Over past decade, enthusiasm for has been rising exponentially. Machine learning was well known, increasing power processors amount data available provided platform deep expand. Although literature applications technology relatively sparse, possible uses are countless especially training, imaging, urodynamics, devices.

参考文章(35)
Massimo Valerio, Patrice Jichlinski, Roland Dahlem, Piergiorgio Tozzi, Anthony R. Mundy, Experimental evaluation of an electromechanical artificial urinary sphincter in an animal model BJU International. ,vol. 112, pp. E337- E343 ,(2013) , 10.1111/J.1464-410X.2012.11728.X
Daniel B. Neill, Using Artificial Intelligence to Improve Hospital Inpatient Care IEEE Intelligent Systems. ,vol. 28, pp. 92- 95 ,(2013) , 10.1109/MIS.2013.51
Lei Ke, Guozheng Yan, Yongbing Wang, Zhiwu Wang, Dasheng Liu, Design and evaluation of an intelligent artificial anal sphincter system powered by an adaptive transcutaneous energy transfer system. International Journal of Artificial Organs. ,vol. 38, pp. 154- 160 ,(2015) , 10.5301/IJAO.5000386
Young H Kim, Chad Goodman, Elan Omessi, Victor Rivera, Michael W Kattan, Timothy B Boone, None, THE CORRELATION OF URODYNAMIC FINDINGS WITH CRANIAL MAGNETIC RESONANCE IMAGING FINDINGS IN MULTIPLE SCLEROSIS The Journal of Urology. ,vol. 159, pp. 972- 976 ,(1998) , 10.1016/S0022-5347(01)63791-1
A. M. TURING, I.—COMPUTING MACHINERY AND INTELLIGENCE Mind. ,vol. 59, pp. 433- 460 ,(1950) , 10.1093/MIND/LIX.236.433
S. Onal, S. Lai-Yuen, P. Bao, A. Weitzenfeld, K. Greene, R. Kedar, S. Hart, Assessment of a semiautomated pelvic floor measurement model for evaluating pelvic organ prolapse on MRI International Urogynecology Journal. ,vol. 25, pp. 767- 773 ,(2014) , 10.1007/S00192-013-2287-4
Sam T Roweis, Lawrence K Saul, Nonlinear Dimensionality Reduction by Locally Linear Embedding Science. ,vol. 290, pp. 2323- 2326 ,(2000) , 10.1126/SCIENCE.290.5500.2323
Shijun Wang, Ronald M. Summers, Machine learning and radiology Medical Image Analysis. ,vol. 16, pp. 933- 951 ,(2012) , 10.1016/J.MEDIA.2012.02.005
DEREK GRIFFITHS, STUART DERBYSHIRE, ANDY STENGER, NEIL RESNICK, BRAIN CONTROL OF NORMAL AND OVERACTIVE BLADDER The Journal of Urology. ,vol. 174, pp. 1862- 1867 ,(2005) , 10.1097/01.JU.0000177450.34451.97
Andrew F. Colhoun, John E. Speich, Lauren F. Cooley, Eugene D. Bell, R. Wayne Barbee, Georgi Guruli, Paul H. Ratz, Adam P. Klausner, Low amplitude rhythmic contraction frequency in human detrusor strips correlates with phasic intravesical pressure waves World Journal of Urology. ,vol. 35, pp. 1255- 1260 ,(2017) , 10.1007/S00345-016-1994-0