Current Urology Reports

, 20:52 | Cite as

Artificial Intelligence in Reproductive Urology

  • Kevin Y. Chu
  • Daniel E. Nassau
  • Himanshu Arora
  • Soum D. Lokeshwar
  • Vinayak Madhusoodanan
  • Ranjith RamasamyEmail author
Men's Health (A Dabaja, Section Editor)
Part of the following topical collections:
  1. Topical Collection on Men’s Health


Purpose of Review

The promise of artificial intelligence (AI) in medicine has been widely theorized over the past couple of decades. It has only been with technological advances over the past few years that physicians and computer scientists have started discovering its true clinical potential. Reproductive urology is a sub-discipline that AI could be of great contribution, as current predictive models and subjectivity within the field have several limitations. We review the literature to summarize recent AI applications in reproductive urology.

Recent Findings

Early AI applications in reproductive urology focused on predicting semen parameters based on questionnaires that identify potential environmental factors and/or lifestyle habits impacting male fertility. AI has shown success in predicting the patient subpopulation most likely to need a genetic workup for azoospermia. With recent advances in image processing, automated sperm detection is a reality. Semen analyses, once a laboratory-only diagnostic test, have moved into health consumer homes with the advent of AI.


AI’s prospects in medicine are considerable and there is strong potential for AI within reproductive urology. Research in identifying the factors that can affect reproductive success either naturally or with assisted reproduction is of paramount importance to move the field forward.


Artificial intelligence Machine learning Reproductive urology Male-factor infertility Artificial neural network Urology 


Compliance with Ethical Standards

Conflict of Interest

Kevin Y. Chu, Daniel E. Nassau, Himanshu Arora, Soum Lokeshwar, Vinayak Madhusoodanan, and Ranjith Ramasamy each declare no potential conflicts of interest.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.


Papers of particular interest, published recently, have been highlighted as: • Of importance

  1. 1.
    Jarvi K, Lau S, Lo K, Grober E, Trussell J, Hotaling J, et al. PD13–04 Results of a North American survey on the characteristics of men being assessed in male infertility clinics: the andrology research consortium. J Urol [Internet]. 2017 Apr [cited 2019 May 8]; Available from:
  2. 2.
    Practice Committee of the American Society for Reproductive Medicine. Diagnostic evaluation of the infertile female: a committee opinion. Fertil Steril. 2015;103(6):e44–50.Google Scholar
  3. 3.
    Siristatidis C, Vogiatzi P, Pouliakis A, Trivella M, Papantoniou N, Bettocchi S. Predicting IVF outcome: a proposed web-based system using artificial intelligence. In Vivo. 2016;30(4):507–12.PubMedGoogle Scholar
  4. 4.
    • Khosravi P, Kazemi E, Zhan Q, Malmsten JE, Toschi M, Zisimopoulos P, et al. Deep learning enables robust assessment and selection of human blastocysts after in vitro fertilization. NPJ Digit Med. 2019;2(1):21 Novel artificial intelligence approach to grading human embryos during the in vitro fertilization (IVF) process. Currently the grading process is subjective with variation across embryology centers, and the possibility of introducing objectivity may lead to better IVF outcomes. CrossRefGoogle Scholar
  5. 5.
    Altman RB. Artificial intelligence (AI) systems for interpreting complex medical datasets. Clin Pharmacol Ther. 2017;101(5):585–6.CrossRefGoogle Scholar
  6. 6.
    Jordan MI, Mitchell TM. Machine learning: trends, perspectives, and prospects. Science. 2015;349(6245):255–60.CrossRefGoogle Scholar
  7. 7.
    Gandhi S, Mosleh W, Shen J, Chow C-M. Automation, machine learning, and artificial intelligence in echocardiography: a brave new world. Echocardiogr Mt Kisco N. 2018;35(9):1402–18.CrossRefGoogle Scholar
  8. 8.
    Schmidhuber J. Deep learning in neural networks: an overview. Neural Netw. 2015;61:85–117.CrossRefGoogle Scholar
  9. 9.
    Lin J, Sun X-X. Predictive modeling in reproductive medicine. Reprod Dev Med. 2018;2(4):224.CrossRefGoogle Scholar
  10. 10.
    Tran BX, Vu GT, Ha GH, Vuong Q-H, Ho M-T, Vuong T-T, et al. Global evolution of research in artificial intelligence in health and medicine: a bibliometric study. J Clin Med. 2019;8(3):360.CrossRefGoogle Scholar
  11. 11.
    Giger ML. Machine learning in medical imaging. J Am Coll Radiol. 2018;15(3 Pt B):512–20.CrossRefGoogle Scholar
  12. 12.
    Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism. 2017;69S:S36–40.CrossRefGoogle Scholar
  13. 13.
    Sherbet GV, Woo WL, Dlay S. Application of artificial intelligence-based technology in cancer management: a commentary on the deployment of artificial neural networks. Anticancer Res. 2018;38(12):6607–13.CrossRefGoogle Scholar
  14. 14.
    Hemal AK, Menon M. Robotics in urology. Curr Opin Urol. 2004;14(2):89–93.CrossRefGoogle Scholar
  15. 15.
    Anagnostou T, Remzi M, Lykourinas M, Djavan B. Artificial neural networks for decision-making in urologic oncology. Eur Urol. 2003;43(6):596–603.CrossRefGoogle Scholar
  16. 16.
    Zheng S, Sun FL, Zhang HJ, Shi WZ, Ma JH. Current applications of artificial intelligence in tumor histopathology. Zhonghua Zhong Liu Za Zhi. 2018;40(12):885–9.PubMedGoogle Scholar
  17. 17.
    Oishi Y, Kitta T, Shinohara N, Nosato H, Sakanashi H, Murakawa M. Automated diagnosis of prostate cancer location by artificial intelligence in multiparametric MRI. Eur Urol Suppl. 2018;17(2):e888–9.CrossRefGoogle Scholar
  18. 18.
    De Perrot T, Hofmeister J, Burgermeister S, et al. Differentiating kidney stones from phleboliths in unenhanced low-dose computed tomography using radiomics and machine learning. Eur Radiol 2019;1–7. Scholar
  19. 19.
    Ozkan IA, Koklu M, Sert IU. Diagnosis of urinary tract infection based on artificial intelligence methods. Comput Methods Prog Biomed. 2018 Nov;166:51–9.CrossRefGoogle Scholar
  20. 20.
    Taylor RA, Moore CL, Cheung K-H, Brandt C. Predicting urinary tract infections in the emergency department with machine learning. PLoS One. 2018;13(3):e0194085.CrossRefGoogle Scholar
  21. 21.
    Cestari A. Predictive models in urology. Urologia. 2013;80(1):42–5.CrossRefGoogle Scholar
  22. 22.
    Abbod MF, Catto JWF, Linkens DA, Hamdy FC. Application of artificial intelligence to the management of urological cancer. J Urol. 2007;178(4 Pt 1):1150–6.CrossRefGoogle Scholar
  23. 23.
    Catto JWF, Linkens DA, Abbod MF, Chen M, Burton JL, Feeley KM, et al. Artificial intelligence in predicting bladder cancer outcome: a comparison of neuro-fuzzy modeling and artificial neural networks. Clin Cancer Res. 2003;9(11):4172–7.PubMedGoogle Scholar
  24. 24.
    Wong NC, Shayegan B. Patient centered care for prostate cancer-how can artificial intelligence and machine learning help make the right decision for the right patient? Ann Transl Med. 2019;7(Suppl 1):S1.CrossRefGoogle Scholar
  25. 25.
    Gil D, Girela JL, De Juan J, Gomez-Torres MJ, Johnsson M. Predicting seminal quality with artificial intelligence methods. Expert Syst Appl. 2012;39(16):12564–73.CrossRefGoogle Scholar
  26. 26.
    Candemir C. Estimating the semen quality from life style using fuzzy radial basis functions. Int J Mach Learn Comput. 2018;8(1):44–8.CrossRefGoogle Scholar
  27. 27.
    El-Shafeiy E, El-Desouky A, El-Ghamrawy S. An optimized artificial neural network approach based on sperm whale optimization algorithm for predicting fertility quality – studies in informatics and control – ICI Bucharest. Stud Inform Control. 2018;27(3):349–58.CrossRefGoogle Scholar
  28. 28.
    Fallah A, Mohammad-Hasani A, Colagar AH. Zinc is an essential element for male fertility: a review of Zn roles in men’s health, germination, sperm quality, and fertilization. J Reprod Infertil. 2018;19(2):69–81.PubMedPubMedCentralGoogle Scholar
  29. 29.
    Vickram AS, Kamini AR, Das R, Pathy MR, Parameswari R, Archana K, et al. Validation of artificial neural network models for predicting biochemical markers associated with male infertility. Syst Biol Reprod Med. 2016;62(4):258–65.CrossRefGoogle Scholar
  30. 30.
    Ma Y, Chen B, Wang H, Hu K, Huang Y. Prediction of sperm retrieval in men with non-obstructive azoospermia using artificial neural networks: leptin is a good assistant diagnostic marker. Hum Reprod. 2011;26(2):294–8.CrossRefGoogle Scholar
  31. 31.
    Gudeloglu A, Parekattil SJ. Update in the evaluation of the azoospermic male. Clinics. 2013;68(Suppl 1):27–34.CrossRefGoogle Scholar
  32. 32.
    Akinsal EC, Haznedar B, Baydilli N, Kalinli A, Ozturk A, Ekmekçioğlu O. Artificial neural network for the prediction of chromosomal abnormalities in azoospermic males. Urol J. 2018;15(3):122–5.PubMedGoogle Scholar
  33. 33.
    WHO | WHO laboratory manual for the examination and processing of human semen [internet]. WHO. [cited 2019 May 14]. Available from:
  34. 34.
    Thirumalaraju P, Bormann CL, Kanakasabapathy M, Doshi F, Souter I, Dimitriadis I, et al. Automated sperm morpshology testing using artificial intelligence. Fertil Steril. 2018 Sep;110(4):e432.CrossRefGoogle Scholar
  35. 35.
    Haugen TB, Andersen JM, Witczak O, Hammer HL, Hicks SA, Borgli RJ, et al. VISEM: a multimodal video dataset of human spermatozoa. 2019 [cited 2019 May 6]; Available from:
  36. 36.
    Yu S, Rubin M, Geevarughese S, Pino JS, Rodriguez HF, Asghar W. Emerging technologies for home-based semen analysis. Andrology. 2018;6(1):10–9.CrossRefGoogle Scholar
  37. 37.
    Kobori Y. Home testing for male factor infertility: a review of current options. Fertil Steril. 2019;111(5):864–70.CrossRefGoogle Scholar
  38. 38.
    Agarwal A, Panner Selvam MK, Sharma R, Master K, Sharma A, Gupta S, et al. Home sperm testing device versus laboratory sperm quality analyzer: comparison of motile sperm concentration. Fertil Steril. 2018;110(7):1277–84.CrossRefGoogle Scholar
  39. 39.
    Tsai V, Zhuang B. An at-home system that adapts to different types of mobile phones for measuring sperm motility--- verification of its performance of Artificial Intelligence (AI) sperm image recognition with cloud computing. J Urol 2019;201:e681.Google Scholar
  40. 40.
    Verheyen G, Popovic-Todorovic B, Tournaye H. Processing and selection of surgically-retrieved sperm for ICSI: a review. Basic Clin Androl. 2017;27(1):6.CrossRefGoogle Scholar
  41. 41.
    • Curchoe CL, Bormann CL. Artificial intelligence and machine learning for human reproduction and embryology presented at ASRM and ESHRE 2018. J Assist Reprod Genet [Internet]. 2019 Jan 28 [cited 2019 Apr 30]; Available from: Summary on recent artificial intelligence studies presented at the American Society for Reproductive Medicine (ASRM) and European Society of Human Reproduction and Embryology (ESHRE) conferences. CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Kevin Y. Chu
    • 1
  • Daniel E. Nassau
    • 2
  • Himanshu Arora
    • 1
  • Soum D. Lokeshwar
    • 1
  • Vinayak Madhusoodanan
    • 1
  • Ranjith Ramasamy
    • 1
    Email author
  1. 1.Department of UrologyUniversity of Miami Miller School of MedicineMiamiUSA
  2. 2.Department of Urology, Lenox Hill HospitalZucker School of Medicine at Hofstra/NorthwellNew YorkUSA

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