Prediction of semen quality using artificial neural network.
Artificial neural network
Concentration
Semen analysis
Semen characteristics
Spermatozoa
Journal
Journal of applied biomedicine
ISSN: 1214-0287
Titre abrégé: J Appl Biomed
Pays: Poland
ID NLM: 101221755
Informations de publication
Date de publication:
Sep 2019
Sep 2019
Historique:
received:
11
12
2018
accepted:
05
09
2019
entrez:
15
12
2021
pubmed:
1
9
2019
medline:
1
9
2019
Statut:
ppublish
Résumé
Examination of semen characteristics is routinely performed for fertility status investigation of the male partner of an infertile couple as well as for evaluation of the sperm donor candidate. A useful tool for preliminary assessment of semen characteristics might be an artificial neural network. Thus, the aim of the present study was to construct an artificial neural network, which could be used for predicting the result of semen analysis based on the basic questionnaire data. On the basis of eleven survey questions two models of artificial neural networks to predict semen parameters were developed. The first model aims to predict the overall performance and profile of semen. The second network was developed to predict the concentration of sperm. The network to evaluate sperm concentration proved to be the most efficient. 92.93% of the patients in the learning process were properly qualified for the group with a correct or incorrect result, while the result for the test set was 85.71%. This study suggests that an artificial neural network based on eleven survey questions might be a valuable tool for preliminary evaluation and prediction of the semen profile.
Identifiants
pubmed: 34907698
doi: 10.32725/jab.2019.015
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
167-174Déclaration de conflit d'intérêts
The authors report no conflicts of interest in this work.
Références
Ansari D, Nilsson J, Andersson R, Regnér S, Tingstedt B, Andersson B (2013). Artificial neural networks predict survival from pancreatic cancer after radical surgery. Am J Surg 205(1): 1-7. DOI: 10.1016/j.amjsurg.2012.05.032.
pubmed: 23245432
doi: 10.1016/j.amjsurg.2012.05.032
Bhardwaj A, Tiwari A (2015). Breast cancer diagnosis using genetically optimized neural network model. Expert Syst Appl 42(10): 4611-4620. DOI: 10.1016/j.eswa.2015.01.065.
doi: 10.1016/j.eswa.2015.01.065
Böhlandt A, Schierl R, Diemer J, Koch C, Bolte G, Kiranoglu M, et al. (2012). High concentrations of cadmium, cerium and lanthanum in indoor air due to environmental tobacco smoke. Sci Total Environ 414: 738-741. DOI: 10.1016/j.scitotenv.2011.11.017.
pubmed: 22137652
doi: 10.1016/j.scitotenv.2011.11.017
Buciński A, Baczek T, Kaliszan R, Nasal A, Krysiński J, Załuski J (2005). Artificial neural network analysis of patient and treatment variables as a prognostic tool in breast cancer after mastectomy. Adv Clin Exp Med 14(5): 973-979.
Buciński A, Wnuk M, Goryński K, Giza A, Kochańczyk J, Nowaczyk A, et al. (2009). Artificial neural networks analysis used to evaluate the molecular interactions between selected drugs and human α1-acid glycoprotein. J Pharm Biomed Anal 50(4): 591-596. DOI: 10.1016/j.jpba.2008.11.005.
pubmed: 19117712
doi: 10.1016/j.jpba.2008.11.005
Buscema M (2002). A brief overview and introduction to artificial neural networks. Subst Use Misuse 37(8-10): 1093-1148. DOI: 10.1081/JA-120004171.
pubmed: 12180558
doi: 10.1081/JA-120004171
Cooper TG, Noonan E, Von Eckardstein S, Auger J, Baker HW, Behre HM, et al. (2010). World Health Organization reference values for human semen characteristics. Hum Reprod Update 16(3): 231-245. DOI: 10.1093/humupd/dmp048.
pubmed: 19934213
doi: 10.1093/humupd/dmp048
Dirks-Naylor AJ (2015). The benefits of coffee on skeletal muscle. Life Sci 143: 182-186. DOI: 10.1016/j.lfs.2015.11.005.
pubmed: 26546720
doi: 10.1016/j.lfs.2015.11.005
Garrido N, Zuzuarregui J, Meseguer M, Simon C, Remohi J, Pellicer A (2002). Sperm and oocyte donor selection and management: Experience of a 10 year follow-up of more than 2100 candidates. Hum Reprod 17: 3142-3148. DOI: 10.1093/humrep/17.12.3142.
pubmed: 12456614
doi: 10.1093/humrep/17.12.3142
Gil D, Girela JL, De Juan J, Gomez-Torres MJ, Johnsson M (2012). Predicting seminal quality with artificial intelligence methods. Expert Syst Appl 39(16): 12564-12573. DOI: 10.1016/j.eswa.2012.05.028.
doi: 10.1016/j.eswa.2012.05.028
Girela JL, Gil D, Johnsson M, Gomez-Torres MJ, De Juan J (2013). Semen parameters can be predicted from environmental factors and lifestyle using artificial intelligence methods. Biol Reprod 88(4): 1-8. DOI: 10.1095/biolreprod.112.104653.
pubmed: 23446456
doi: 10.1095/biolreprod.112.104653
Goryński K, Safian I, Gradzki W, Marszałł MP, Krysiński J, Goryński S, et al. (2014). Artificial neural networks approach to early lung cancer detection. Cent Eur J Med 9: 632-641. DOI: 10.2478/s11536-013-0327-6.
doi: 10.2478/s11536-013-0327-6
Grioni S, Agnoli C, Sieri S, Pala V, Ricceri F, Masala G, et al. (2015). Espresso coffee consumption and risk of coronary heart disease in a large italian cohort. PLoS One 10(5): e0126550. DOI: 10.1371/journal.pone.0126550.
pubmed: 25946046
doi: 10.1371/journal.pone.0126550
Iraji MS (2019a). Combining predictors for multi-layer architecture of adaptive fuzzy inference system. Cogn Syst Res 53: 71-84. DOI: 10.1016/j.cogsys.2018.05.005.
doi: 10.1016/j.cogsys.2018.05.005
Iraji MS (2019b). Prediction of fetal state from the cardiotocogram recordings using neural network models. Artif Intell Med 96: 33-44. DOI: 10.1016/j.artmed.2019.03.005.
pubmed: 31164209
doi: 10.1016/j.artmed.2019.03.005
Jensen TK, Swan SH, Skakkebaek NE, Rasmussen S, Jørgensen N (2010). Caffeine intake and semen quality in a population of 2,554 young danish men. Am J Epidemiol 171(8): 883-891. DOI: 10.1093/aje/kwq007.
pubmed: 20338976
doi: 10.1093/aje/kwq007
Jurewicz J, Radwan M, Sobala W, Ligocka D, Radwan P, Bochenek M, Hanke W (2014a). Lifestyle and semen quality: Role of modifiable risk factors. Syst Biol Reprod Med 60(1): 43-51. DOI: 10.3109/19396368.2013.840687.
pubmed: 24074254
doi: 10.3109/19396368.2013.840687
Jurewicz J, Radwan M, Sobala W, Radwan P, Bochenek M, Hanke W (2014b). Effects of occupational exposure - is there a link between exposure based on an occupational questionnaire and semen quality? Syst Biol Reprod Med 60(4): 227-233. DOI: 10.3109/19396368.2014.907837.
pubmed: 24702586
doi: 10.3109/19396368.2014.907837
Knyazev AV, Lashuk I (2007). Steepest descent and conjugate gradient methods with variable preconditioning. SIAM J Matrix Anal Appl 29(4): 1267-1280. DOI: 10.1137/060675290.
doi: 10.1137/060675290
Lalos A, Lalos O, Jacobsson L, Von Schoultz B (1985). Psychological reactions to the medical investigation and surgical treatment of infertility. Gynecol Obstet Invest 20(4): 209-217. DOI: 10.1159/000298996.
pubmed: 4085924
doi: 10.1159/000298996
Luenberger DG, Ye Y (2008). Linear and nonlinear programming, 3rd ed. New York: Springer Science+Business Media, LLC.
Ma Y, Chen B, Wang HX, Hu K, Huang YR (2011). Prediction of sperm retrieval in men with non-obstructive azoospermia using artificial neural networks: Leptin is a good assistant diagnostic marker. Hum Reprod 26(2): 294-298. DOI: 10.1093/humrep/deq337.
pubmed: 21138907
doi: 10.1093/humrep/deq337
Martini AC, Molina RI, Estofan D, Senestrari D, Fiol De Cuneo M, Ruiz RD (2004). Effects of alcohol and cigarette consumption on human seminal quality. Fertil Steril 82(2): 374-377. DOI: 10.1016/j.fertnstert.2004.03.022.
pubmed: 15302286
doi: 10.1016/j.fertnstert.2004.03.022
Marzec-Wróblewska U, Kamiński P, Łakota P, Ludwikowski G, Szymański M, Wasilow K, et al. (2015a). Determination ofrare earth elements in human sperm and association with semen quality. Arch Environ Contam Toxicol 69(2): 191-201. DOI: 10.1007/s00244-015-0143-x.
pubmed: 25762379
doi: 10.1007/s00244-015-0143-x
Marzec-Wróblewska U, Kamiński P, Łakota P, Szymański M, Wasilow K, Ludwikowski G, et al. (2015b). The employment of IVF techniques for establishment of sodium, copper and selenium, impact upon human sperm quality. Reprod Fert Develop 28(10): 1518-1525. DOI: 10.1071/Rd15041.
pubmed: 25786584
doi: 10.1071/Rd15041
Marzec-Wróblewska U, Kamiński P, Łakota P, Szymański M, Wasilow K, Ludwikowski G, et al. (2011). Zinc and iron concentration and SOD activity in human semen and seminal plasma. Biol Trace Elem Res 143(1): 167-177. DOI: 10.1007/s12011-010-8868-x.
pubmed: 20924714
doi: 10.1007/s12011-010-8868-x
Mekruksavanich S (2016). A prediction model for influenza epidemics using artificial neural networks. Far East J Electron Commun 16(1): 131-146. DOI: 10.17654/EC016010131.
doi: 10.17654/EC016010131
Milardi D, Grande G, Sacchini D, Astorri AL, Pompa G, Giampietro A, et al. (2012). Male fertility and reduction in semen parameters: A single tertiary-care center experience. Int J Endocrinol 2012: 649149. DOI: 10.1155/2012/649149.
pubmed: 22319527
doi: 10.1155/2012/649149
Office on Smoking and Helath (US) (2006). The health consequences of involuntary exposure to tobacco smoke: A report of the surgeon general. Atlanta, GA: US Department of Health and Human Services, Centers for Disease Control and Prevention. [online] [cit. 2019-06-24]. Available from: https://www.ncbi.nlm.nih.gov/books/NBK44324/
Pant N, Kumar G, Upadhyay A, Gupta Y, Chaturvedi P (2015). Correlation between lead and cadmium concentration and semen quality. Andrologia 47(8): 887-891. DOI: 10.1111/and.12342.
pubmed: 25228328
doi: 10.1111/and.12342
Sahoo AJ, Kumar Y (2014). Seminal quality prediction using data mining methods. Technol Health Care 22(4): 531-545. DOI: 10.3233/THC-140816.
pubmed: 24898862
doi: 10.3233/THC-140816
Samli MM, Dogan I (2004). An artificial neural network for predicting the presence of spermatozoa in the testes of men with nonobstructive azoospermia. J Urol 171(6 Pt 1): 2354-2357. DOI: 10.1097/01.ju.0000125272.03182.c3.
pubmed: 15126820
doi: 10.1097/01.ju.0000125272.03182.c3
Saritas I, Ozkan IA, Sert IU (2010). Prognosis of prostate cancer by artificial neural networks. Expert Syst Appl 37(9): 6646-6650. DOI: 10.1016/j.eswa.2010.03.056.
doi: 10.1016/j.eswa.2010.03.056
Sharma R, Biedenharn KR, Fedor JM, Agarwal A (2013). Lifestyle factors and reproductive health: Taking control of your fertility. Reprod Biol Endocrinol 11: 66. DOI: 10.1186%2F1477-7827-11-66.
pubmed: 23870423
doi: 10.1186/1477-7827-11-66
Siristatidis C, Pouliakis A, Chrelias C, Kassanos D (2011). Artificial intelligence in IVF: A need. Syst Biol Reprod Med 57(4): 179-185. DOI: 10.3109/19396368.2011.558607.
pubmed: 21375363
doi: 10.3109/19396368.2011.558607
Slezakova K, Pereira M, Alvim-Ferraz M (2009). Influence of tobacco smoke on the elemental composition of indoor particles of different sizes. Atmos Environ 43(3): 486-493. DOI: 10.1016/j.atmosenv.2008.10.017.
doi: 10.1016/j.atmosenv.2008.10.017
Spelt L, Nilsson J, Andersson R, Andersson B (2013). Artificial neural networks - a method for prediction of survival following liver resection for colorectal cancer metastases. Europ J Surg Oncol 39(6): 648-654. DOI: 10.1016/j.ejso.2013.02.024.
pubmed: 23514791
doi: 10.1016/j.ejso.2013.02.024
Swan SH, Brazil C, Drobnis EZ, Liu F, Kruse RL, Hatch M, et al. (2003). Geographic differences in semen quality of fertile U.S. males. Environ Health Perspect 111(4): 414-420. DOI: 10.1289/ehp.5927.
pubmed: 12676592
doi: 10.1289/ehp.5927
Vickram AS, Kamini AR, Das R, Pathy MR, Parameswari R, Archana K, Sridharan TB (2016). Validation of artificial neural network models for predicting biochemical markers associated with male infertility. Syst Biol Reprod Med 62(4): 258-265. DOI: 10.1080/19396368.2016.1185654.
pubmed: 27327177
doi: 10.1080/19396368.2016.1185654
Vickram AS, Raja D, Srinivas MS, Kamini AR, Jayaraman G, Sridharan TB (2013). Prediction of Zn concentration in human seminal plasma of normospermia samples by artificial neural networks (ANN). J Assist Reprod Genet 30(4): 453-459. DOI: 10.1007/s10815-012-9926-4.
pubmed: 23307446
doi: 10.1007/s10815-012-9926-4
Wang B, Dong F, Chen S, Chen M, Bai Y, Tan J, et al. (2016). Phenolic endocrine disrupting chemicals in an urban receiving river (Panlong river) of Yunnan-Guizhou plateau: Occurrence, bioaccumulation and sources. Ecotoxicol Environ Saf 128: 133-142. DOI: 10.1016/j.ecoenv.2016.02.018.
pubmed: 26921547
doi: 10.1016/j.ecoenv.2016.02.018
WHO (2010). Who laboratory manual for examination and processing of human semen, 5th ed. Geneva: WHO Press.
WHO, International Agency for Research on Cancer (2009). Tobacco smoke and involuntary smoking. IARC monographs on the evaluation of carcinogenic risks to humans, No. 83. Lyon: International Agency for Research on Cancer.
Wnuk M, Marszałł M, Zapęcka A, Nowaczyk A, Krysiński J, Romaszko J, et al. (2013). Prediction of antimicrobial activity of imidazole derivatives by artificial neural networks. Cent Eur J Med 8(1): 1-15. DOI: 10.2478/s11536-012-0052-6.
doi: 10.2478/s11536-012-0052-6
Yang H, Chen Q, Zhou N, Sun L, Bao H, Tan L, et al. (2015). Lifestyles associated with human semen quality: Results from marhcs cohort study in Chongqing, China. Medicine (Baltimore) 94(28): e1166. DOI: 10.1097%2FMD.0000000000001166.
pubmed: 26181561
doi: 10.1097/MD.0000000000001166
Zhou N, Cui Z, Yang S, Han X, Chen G, Zhou Z, et al. (2014). Air pollution and decreased semen quality: A comparative study of Chongqing urban and rural areas. Environ Pollut 187: 145-152. DOI: 10.1016/j.envpol.2013.12.030.
pubmed: 24491300
doi: 10.1016/j.envpol.2013.12.030
Zou J, Han Y, So SS (2008). Overview of artificial neural networks. In: Livingstone DJ (Ed.). Artificial Neural Networks. Methods Mol Biol 485: 15-23. DOI: 10/1007/978-1-60327-101-1.
pubmed: 19065803
doi: 10.1007/978-1-60327-101-1_2