Artificial Neural Network for the Prediction of Chromosomal Abnormalities in Azoospermic Males

Emre Can Akinsal, Bulent Haznedar, Numan Baydilli, Adem Kalinli, Ahmet Ozturk, O?uz Ekmekçio?lu

Abstract


195

Purpose: To evaluate whether an artifical neural network helps to diagnose any chromosomal abnormalities in azoospermic males. Materials and Methods: The data of azoospermic males attending to a tertiary academic referral center were evaluated retrospectively. Height, total testicular volume, follicle stimulating hormone, luteinising hormone, total testosterone and ejaculate volume of the patients were used for the analyses. In artificial neural network, the data of 310 azoospermics were used as the education and 115 as the test set. Logistic regression analyses and discriminant analyses were performed for statistical analyses. The tests were re-analysed with a neural network. Results: Both logistic regression analyses and artificial neural network predicted the presence or absence of chromosomal abnormalities with more than 95% accuracy. Conclusion: The use of artificial neural network model has yielded satisfactory results in terms of distinguishing patients whether they have any chromosomal abnormality or not.

Full Text:

PDF

104

References


Lamb DJ, Niederberger CS. Artificial intelligence in medicine and male infertility. World J Urol. 1993; 11:129-36.

Cooper TG, Noonan E, von Eckardstein S, et al. World Health Organization reference values for human semen characteristics. Hum Reprod Update 2010; 16: 231-45.

Hagan MT, Menhaj MB. Training feedforward networks with the Marquardt algorithm. IEEE Trans. Neural Netw. 1994; 5: 989-93.

Chen S, Billings SA, Grant PM. Non-linear system identification using neural networks. Int J Control. 1990; 51: 1191–215.

Practice Committee of American Society for Reproductive Medicine in collaboration with Society for Male Reproduction and Urology. Evaluation of the azoospermic male. Fertil Steril. 2008; 90 Suppl 5: 74–7.

Sigman S, Lipshultz L, Howards S. Office evaluation of the subfertile male; In: Lipshultz L, Howards S, Niederberger C (eds). Infertility in the Male. 4th ed. New York: Cambridge University Press, 2009; pp 153–76.

Foresta C, Ferlin A, Gianaroli L, Dallapiccola B. Guidelines for the appropriate use of genetic tests in infertile couples. Eur J Hum Genet. 2002; 10: 303-12.

Nakamura Y, Kitamura M, Nishimura K, et al. Chromosomal variants among 1790 infertile men. Int J Urol. 2001; 8: 49–52.

Parekattil SJ, Fisher HA, Kogan BA. Neural network using combined urine nuclear matrix protein-22, monocyte chemoattractant protein-1 and urinary intercellular adhesion

molecule-1 to detect bladder cancer. J Urol. 2003; 169: 917-20.

Kalra P, Togami J, Bansal BSG, et al. A neurocomputational model for prostate carcinoma detection. Cancer. 2003; 98: 1849-

Cummings JM, Boullier JA, Izenberg SD, Kitchens DM, Kothandapani RV. Prediction of spontaneous ureteral calculous passage by an artificial neural network. J Urol. 2000; 164: 326-8.

Wadie BS, Badawi AM, Abdelwahed M, Elemabay SM. Application of artificial neural network in prediction of bladder outlet obstruction: a model based on objective, noninvasive parameters. Urology. 2006; 68:1211-4.

Niederberger C. Neural computation in urology: an orientation. Mol Urol. 2001; 5:133-9.

Hinton GE. How neural networks learn from experience. Sci Am. 1992; 267: 144-51.

Samli MM, Dogan I: An artificial neural network for predicting the presence of spermatozoa in the testes of men with nonobstructive azoospermia. J Urol. 2004; 171: 2354-7.

Wald M, Sparks A, Sandlow J, Van-Voorhis B, Syrop CH, Niederberger CS. Computational models for prediction of IVF/ICSI outcomes with surgically retrieved spermatozoa. Reprod

Biomed Online. 2005; 11: 325-31.

Ramasamy R, Padilla WO, Osterberg EC, et al. A comparison of models for predicting sperm retrieval before microdissection testicular sperm extraction in men with nonobstructive

azoospermia. J Urol. 2013; 189: 638-42.

Ma Y, Chen B, Wang H, Hu K, Huang Y. Prediction of sperm retrieval in men with nonobstructive azoospermia using artificial neural networks: leptin is a good assistant diagnostic marker. Hum Reprod. 2011; 26: 294-8.

Hull MG, Glazener CM, Kelly NJ, et al. Population study of causes, treatment and outcome of infertility. Br Med J (Clin Res Ed). 1985; 291: 1693-7.

Matsumiya K, Namiki M, Takahara S, et al. Clinical study of azoospermia. Int J Androl. 1994; 17: 140-2. Artificial neural network in azoospermia–Akinsal et al.




DOI: http://dx.doi.org/10.22037/uj.v0i0.4029


Creative Commons License 
This work is licensed under a Creative Commons Attribution 3.0 License