Urology
Volume 54, Issue 6 , Pages 945-948 , December 1999

Artificial neural networks in urology: PRO

  • John T Wei

      Affiliations

    • American Foundation for Urologic Disease, Ann Arbor, Michigan, USA
    • Department of Surgery/Urology, The University of Michigan, Ann Arbor, Michigan, USA
    • Corresponding Author InformationReprint requests: John T. Wei, M.D., Section of Urology, University of Michigan Health System, Taubman Health Care Center, 1500 East Medical Center Drive, Ann Arbor, MI 48109-0330
    • ,
  • Ashutosh Tewari

      Affiliations

    • The Josephine Ford Cancer Center, Detroit, Michigan, USA
    • Department of Urology, Henry Ford Health Systems, Detroit, Michigan, USA

Received 2 June 1999 ,Revised 26 July 1999 ,Accepted 26 July 1999.

References 

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  3. Michaels EK, Niederberger CS, Golden RM, et al.  Use of a neural network to predict stone growth after shock wave lithotripsy. Urology. 1998;51:335–338
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  16. Tewari A, Mager J, Kamerer A, et al.  Genetic adaptive probabilistic neural network models in prediction of pathological stage in management of localized prostate cancer (a pilot study). J Urol. 1997;157:A293
  17. Ragde H, Elgamal AA, Snow PB, et al.  Ten-year disease free survival after transperineal sonography-guided iodine-125 brachytherapy with or without 45-gray external beam irradiation in the treatment of patients with clinically localized, low to high Gleason grade prostate carcinoma. Cancer. 1998;83:989–1001
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PII: S0090-4295(99)00341-6

Urology
Volume 54, Issue 6 , Pages 945-948 , December 1999

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