Skip to main content

Advertisement

SpringerLink
Log in

AI Tools for Assessing Human Fertility Using Risk Factors: A State-of-the-Art Review

  • Review
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

Infertility has massively disrupted social and marital life, resulting in stressful emotional well-being. Early diagnosis is the utmost need for faster adaption to respond to these changes, which makes possible via AI tools. Our main objective is to comprehend the role of AI in fertility detection since we have primarily worked to find biomarkers and related risk factors associated with infertility. This paper aims to vividly analyse the role of AI as an effective method in screening, predicting for infertility and related risk factors. Three scientific repositories: PubMed, Web of Science, and Scopus, are used to gather relevant articles via technical terms: (human infertility OR human fertility) AND risk factors AND (machine learning OR artificial intelligence OR intelligent system). In this way, we systematically reviewed 42 articles and performed a meta-analysis. The significant findings and recommendations are discussed. These include the rising importance of data augmentation, feature extraction, explainability, and the need to revisit the meaning of an effective system for fertility analysis. Additionally, the paper outlines various mitigation actions that can be employed to tackle infertility and its related risk factors. These insights contribute to a better understanding of the role of AI in fertility analysis and the potential for improving reproductive health outcomes.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

(Source: PRISMA criteria [117]

Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Taylor, A. (2003). ABC of subfertility: extent of the problem. BMJ: British Medical Journal, 327(7412), 434

  2. https://platform.who.int/data/sexual-and-reproductive-health-and-rights/infertility-data. Accessed 13 July 2023.

  3. Menken, J., Trussell, J., & Larsen, U. (1986). Age and infertility. Science, 233(4771), 1389-1394.

    Article  CAS  PubMed  Google Scholar 

  4. Sharpe, R. M., & Franks, S. (2002). Environment, lifestyle and infertility—an inter-generational issue. Nature Medicine, 8(10), S33-S40.

    Article  CAS  Google Scholar 

  5. Hollmann, M., Runnebaum, B., & Gerhard, I. (1996). Infertility: effects of weight loss on the hormonal profile in obese, infertile women. Human reproduction, 11(9), 1884-1891.

    Article  CAS  PubMed  Google Scholar 

  6. Bala, R., Singh, V., Rajender, S., & Singh, K. (2021). Environment, lifestyle, and female infertility. Reproductive sciences, 28(3), 617-638.

    Article  PubMed  Google Scholar 

  7. Unuane, D., Tournaye, H., Velkeniers, B., & Poppe, K. (2011). Endocrine disorders & female infertility. Best Practice & Research Clinical Endocrinology & Metabolism, 25(6), 861-873.

    Article  CAS  Google Scholar 

  8. Denning, C. R., Sommers, S. C., & Quigley Jr, H. J. (1968). Infertility in male patients with cystic fibrosis. Pediatrics, 41(1), 7-17.

    Article  CAS  PubMed  Google Scholar 

  9. Siristatidis, C., Vogiatzi, P., Pouliakis, A., Trivella, M., Papantoniou, N., & Bettocchi, S. (2016). Predicting IVF outcome: a proposed web-based system using artificial intelligence. in vivo, 30(4), 507–512.

  10. Chiware, T. M., Vermeulen, N., Blondeel, K., Farquharson, R., Kiarie, J., Lundin, K., ... & Toskin, I. (2021). IVF and other ART in low-and middle-income countries: a systematic landscape analysis. Human Reproduction Update, 27(2), 213-228.

    Article  PubMed  Google Scholar 

  11. Islam, M., & Rahaman, A. (2020). Development of smart healthcare monitoring system in IoT environment. SN computer science, 1(3), 1-11.

    Article  Google Scholar 

  12. Rahaman, A., Islam, M. M., Islam, M. R., Sadi, M. S., & Nooruddin, S. (2019). Developing IoT Based Smart Health Monitoring Systems: A Review. Rev. d'Intelligence Artif., 33(6), 435-440.

    Article  Google Scholar 

  13. Botezatu, A., Vladoiu, S., Fudulu, A., Albulescu, A., Plesa, A., Muresan, A., ... & Dinu-Draganescu, D. (2022). Advanced molecular approaches in male infertility diagnosis. Biology of Reproduction.

  14. Chen, Z., Wang, Z., Du, M., & Liu, Z. (2022). Artificial Intelligence in the Assessment of Female Reproductive Function Using Ultrasound: A Review. Journal of Ultrasound in Medicine, 41(6), 1343-1353.

    Article  PubMed  Google Scholar 

  15. Wang, R., Pan, W., Jin, L., Li, Y., Geng, Y., Gao, C., ... & Liao, S. (2019). Artificial intelligence in reproductive medicine. Reproduction, 158(4), R139-R154.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Raimundo, J. M., & Cabrita, P. (2021). Artificial intelligence at assisted reproductive technology. Procedia Computer Science, 181, 442-447.

    Article  Google Scholar 

  17. Dalal, R. J., Gupta, S., & Mishra, A. P. (2020). Artificial intelligence in assisted reproductive technology: Present and future. International Journal of Infertility & Fetal Medicine, 11(3), 61-64.

    Article  Google Scholar 

  18. Naser, M., Mohamed, M. N., & Shehata, L. H. (2021). Artificial Intelligence In Assisted Reproductive Technology.

  19. Ranjini, K., Suruliandi, A., & Raja, S. P. (2020). Machine learning techniques for assisted reproductive technology: A review. Journal of Circuits, Systems and Computers, 29(11), 2030010.

    Article  Google Scholar 

  20. Fernandez, E. I., Ferreira, A. S., Cecílio, M. H. M., Chéles, D. S., de Souza, R. C. M., Nogueira, M. F. G., & Rocha, J. C. (2020). Artificial intelligence in the IVF laboratory: overview through the application of different types of algorithms for the classification of reproductive data. Journal of Assisted Reproduction and Genetics, 37(10), 2359-2376.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Larsen, U. (2005). Research on infertility: which definition should we use. Fertility and sterility, 83(4), 846-852.

    Article  PubMed  Google Scholar 

  22. Abebe, M. S., Afework, M., & Abaynew, Y. (2020). Primary and secondary infertility in Africa: systematic review with meta-analysis. Fertility Research and Practice, 6(1), 1-11.

    Article  Google Scholar 

  23. Fernandes, J. R., & Banerjee, A. (2017). Obesity, Spermatogenesis, and Male Infertility. In Male Infertility: Understanding, Causes and Treatment (pp. 167–182). Springer, Singapore.

  24. Palmer, N. O., Bakos, H. W., Fullston, T., & Lane, M. (2012). Impact of obesity on male fertility, sperm function and molecular composition. Spermatogenesis, 2(4), 253-263.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Lue, T. F. (2000). Erectile dysfunction. New England journal of medicine, 342(24), 1802-1813.

    Article  CAS  PubMed  Google Scholar 

  26. Kubin, M., Wagner, G., & Fugl-Meyer, A. R. (2003). Epidemiology of erectile dysfunction. International Journal of Impotence Research, 15(1), 63-71.

    Article  CAS  PubMed  Google Scholar 

  27. Kumar, N., & Singh, A. K. (2015). Trends of male factor infertility, an important cause of infertility: A review of literature. Journal of human reproductive sciences, 8(4), 191–196.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Agarwal, A., Mulgund, A., Hamada, A., & Chyatte, M. R. (2015). A unique view on male infertility around the globe. Reproductive biology and endocrinology: RB&E, 13, 37.

    Article  Google Scholar 

  29. Ter Keurst, A., Boivin, J., & Gameiro, S. (2016). Women's intentions to use fertility preservation to prevent age-related fertility decline. Reproductive biomedicine online, 32(1), 121-131.

    Article  PubMed  Google Scholar 

  30. Melo, A. S., Ferriani, R. A., & Navarro, P. A. (2015). Treatment of infertility in women with polycystic ovary syndrome: approach to clinical practice. Clinics, 70(11), 765-769.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Wheeler, J. M. (1989). Epidemiology of endometriosis-associated infertility. The Journal of reproductive medicine, 34(1), 41-46

    CAS  PubMed  Google Scholar 

  32. Balen, A. H., & Rutherford, A. J. (2007). Managing anovulatory infertility and polycystic ovary syndrome. BMJ (Clinical research ed.), 335(7621), 663–666.

    Article  PubMed  Google Scholar 

  33. Vigil, P., Lyon, C., Flores, B., Rioseco, H., & Serrano, F. (2017). Ovulation, a sign of health. The Linacre quarterly, 84(4), 343–355.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Moridi, A., Roozbeh, N., Yaghoobi, H., Soltani, S., Dashti, S., Shahrahmani, N., & Banaei, M. (2019). Etiology and risk factors associated with infertility. Int J Women’s Heal Reprod Sci, 7(3), 346-53.

    Article  Google Scholar 

  35. Kahn, B. E., & Brannigan, R. E. (2017). Obesity and male infertility. Current Opinion in Urology, 27(5), 441-445.

    Article  PubMed  Google Scholar 

  36. Sharma, R., Biedenharn, K. R., Fedor, J. M., & Agarwal, A. (2013). Lifestyle factors and reproductive health: taking control of your fertility. Reproductive biology and endocrinology, 11(1), 1-15.

    Article  Google Scholar 

  37. Henry-Suchet, J., & Loffredo, V. (1980). Chlamydiae and myco-plasma genital infection in salpingitis and tubal sterility. Chlamydiae and myco-plasma genital infection in salpingitis and tubal sterility., 1.

  38. Burns, L. H., & Covington, S. N. (2006). Psychology of infertility. Infertility counseling: A comprehensive handbook for clinicians, 1–19.

  39. Leke, R. J., Oduma, J. A., Bassol-Mayagoitia, S., Bacha, A. M., & Grigor, K. M. (1993). Regional and geographical variations in infertility: effects of environmental, cultural, and socioeconomic factors. Environmental health perspectives, 101(suppl 2), 73-80.

    Article  PubMed  PubMed Central  Google Scholar 

  40. McQueen, D. B., Zhang, J., & Robins, J. C. (2019). Sperm DNA fragmentation and recurrent pregnancy loss: a systematic review and meta-analysis. Fertility and sterility, 112(1), 54-60.

    Article  PubMed  Google Scholar 

  41. Evenson, D. P., & Wixon, R. (2006). Clinical aspects of sperm DNA fragmentation detection and male infertility. Theriogenology, 65(5), 979-991.

    Article  CAS  PubMed  Google Scholar 

  42. Simon, L., Emery, B., & Carrell, D. T. (2019). Sperm DNA fragmentation: consequences for reproduction. In Genetic Damage in Human Spermatozoa (pp. 87–105). Springer, Cham.

  43. Ribas-Maynou, J., & Benet, J. (2019). Single and Double Strand Sperm DNA Damage: Different Reproductive Effects on Male Fertility. Genes, 10(2), 105.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Yang, H., Li, G., Jin, H., Guo, Y., & Sun, Y. (2019). The effect of sperm DNA fragmentation index on assisted reproductive technology outcomes and its relationship with semen parameters and lifestyle. Translational andrology and urology, 8(4), 356–365.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Bieniek, J. M., Drabovich, A. P., & Lo, K. C. (2016). Seminal biomarkers for the evaluation of male infertility. Asian journal of andrology, 18(3), 426.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Chia, S. E., Tay, S. K., & Lim, S. T. (1998). What constitutes a normal seminal analysis? Semen parameters of 243 fertile men. Human reproduction (Oxford, England), 13(12), 3394-3398.

    Article  CAS  PubMed  Google Scholar 

  47. Lotti, F., & Maggi, M. (2018). Sexual dysfunction and male infertility. Nature Reviews Urology, 15(5), 287-307.

    Article  PubMed  Google Scholar 

  48. Mohseni, M. G., Hosseini, S. R., Alizadeh, F., & Rangzan, N. (2014). Serum testosterone and gonadotropins levels in patients with premature ejaculation: A comparison with normal men. Advanced biomedical research, 3, 6.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Iammarrone, E., Balet, R., Lower, A. M., Gillott, C., & Grudzinskas, J. G. (2003). Male infertility. Best practice & research Clinical obstetrics & gynaecology, 17(2), 211-229.

    Article  CAS  Google Scholar 

  50. Balen, A. H., Morley, L. C., Misso, M., Franks, S., Legro, R. S., Wijeyaratne, C. N., Stener-Victorin, E., Fauser, B. C., Norman, R. J., & Teede, H. (2016). The management of anovulatory infertility in women with polycystic ovary syndrome: an analysis of the evidence to support the development of global WHO guidance. Human reproduction update, 22(6), 687–708.

    Article  PubMed  Google Scholar 

  51. Roe, A. H., & Dokras, A. (2011). The diagnosis of polycystic ovary syndrome in adolescents. Reviews in obstetrics & gynecology, 4(2), 45–51.

    Google Scholar 

  52. Al Chami, A., & Saridogan, E. (2017). Endometrial Polyps and Subfertility. Journal of obstetrics and gynaecology of India, 67(1), 9–14.

    Article  PubMed  Google Scholar 

  53. Purohit, P., & Vigneswaran, K. (2016). Fibroids and Infertility. Current obstetrics and gynecology reports, 5, 81–88.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Guo, X. C., & Segars, J. H. (2012). The impact and management of fibroids for fertility: an evidence-based approach. Obstetrics and Gynecology Clinics, 39(4), 521-533.

    PubMed  Google Scholar 

  55. Bulletti, C., Coccia, M. E., Battistoni, S., & Borini, A. (2010). Endometriosis and infertility. Journal of assisted reproduction and genetics, 27(8), 441–447.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Ades, A. E., Price, M. J., Kounali, D., Akande, V. A., Wills, G. S., McClure, M. O., ... & Horner, P. J. (2017). Proportion of tubal factor infertility due to Chlamydia: finite mixture modeling of serum antibody titers. American journal of epidemiology, 185(2), 124-134.

    Article  CAS  PubMed  Google Scholar 

  57. Drollette, C. M., & Badawy, S. Z. (1992). Pathophysiology of pelvic adhesions. Modern trends in preventing infertility. The Journal of reproductive medicine, 37(2), 107-21.

    CAS  PubMed  Google Scholar 

  58. Pellati, D., Mylonakis, I., Bertoloni, G., Fiore, C., Andrisani, A., Ambrosini, G., & Armanini, D. (2008). Genital tract infections and infertility. European Journal of Obstetrics & Gynecology and Reproductive Biology, 140(1), 3-11.

    Article  Google Scholar 

  59. Sellors, J. W., Mahony, J. B., Chernesky, M. A., & Rath, D. J. (1988). Tubal factor infertility: an association with prior chlamydial infection and asymptomatic salpingitis. Fertility and sterility, 49(3), 451-457.

    Article  CAS  PubMed  Google Scholar 

  60. Westrom, L. V. (1996). Chlamydia and its effect on reproduction. Journal of the British Fertility Society, 1(1), 23-30.

    CAS  PubMed  Google Scholar 

  61. Stoop, D., Cobo, A., & Silber, S. (2014). Fertility preservation for age-related fertility decline. The Lancet, 384(9950), 1311-1319.

    Article  Google Scholar 

  62. Garcia, D., Brazal, S., Rodriguez, A., Prat, A., & Vassena, R. (2018). Knowledge of age-related fertility decline in women: a systematic review. European Journal of Obstetrics & Gynecology and Reproductive Biology, 230, 109-118.

    Article  Google Scholar 

  63. Speroff, L. (1994). The effect of aging on fertility. Current opinion in obstetrics & gynecology, 6(2), 115-120.

    Article  CAS  Google Scholar 

  64. Hammoud, A. O., Meikle, A. W., Reis, L. O., Gibson, M., Peterson, C. M., & Carrell, D. T. (2012, December). Obesity and male infertility: a practical approach. In Seminars in reproductive medicine (Vol. 30, No. 06, pp. 486–495). Thieme Medical Publishers.

  65. Trovato G. M. (2012). Behavior, nutrition and lifestyle in a comprehensive health and disease paradigm: skills and knowledge for a predictive, preventive and personalized medicine. The EPMA journal, 3(1), 8.

    Article  PubMed  PubMed Central  Google Scholar 

  66. Kelly-Weeder, S., & Cox, C. L. (2007). The impact of lifestyle risk factors on female infertility. Women & Health, 44(4), 1-23.

    Article  Google Scholar 

  67. Hruska, K. S., Furth, P. A., Seifer, D. B., Sharara, F. I., & Flaws, J. A. (2000). Environmental factors in infertility. Clinical obstetrics and gynecology, 43(4), 821–829.

    Article  CAS  PubMed  Google Scholar 

  68. Edward V. Younglai, Alison C. Holloway, Warren G. Foster. (2005). Environmental and occupational factors affecting fertility and IVF success, Human Reproduction Update, 11 (1), 43–57.

    Article  PubMed  Google Scholar 

  69. Alejandro Oliva, Alfred Spira, Luc Multigner. (2001) Contribution of environmental factors to the risk of male infertility, Human Reproduction, 16 (8), 1768–1776.

    Article  CAS  PubMed  Google Scholar 

  70. Tao, P., Coates, R., & Maycock, B. (2011). The impact of infertility on sexuality: A literature review. The Australasian medical journal, 4(11), 620–627.

    Article  PubMed  PubMed Central  Google Scholar 

  71. Soltanzadeh, S., Zarandi, M. H. F., & Astanjin, M. B. (2016). A hybrid fuzzy clustering approach for fertile and unfertile analysis. In 2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS) (pp. 1–6). IEEE.

  72. Sahoo, A. J., & Kumar, Y. (2014). Seminal quality prediction using data mining methods. Technology and Health Care, 22(4), 531-545.

    Article  PubMed  Google Scholar 

  73. Simfukwe, M., Kunda, D., & Chembe, C. (2015). Comparing naive bayes method and artificial neural network for semen quality categorization. International Journal of Innovative Science, Engineering & Technology, 2(7), 689-694.

    Google Scholar 

  74. Girela, J. L., Gil, D., Johnsson, M., Gomez-Torres, M. J., & De Juan, J. (2013). Semen parameters can be predicted from environmental factors and lifestyle using artificial intelligence methods. Biology of reproduction, 88(4), 99-1.

    Article  PubMed  Google Scholar 

  75. Bidgoli, A. A., Komleh, H. E., & Mousavirad, S. J. (2015, November). Seminal quality prediction using optimized artificial neural network with genetic algorithm. In 2015 9th International Conference on Electrical and Electronics Engineering (ELECO) (pp. 695–699). IEEE.

  76. Ahmed, M. T., & Imtiaz, M. N. Prediction of Seminal Quality Based on Naïve Bayes Approach, 4(2), 2020. PUST.

  77. Engy, E. L., Ali, E. L., & Sally, E. G. (2018). An optimized artificial neural network approach based on sperm whale optimization algorithm for predicting fertility quality. Stud Informatics Control, 27(3), 349-358.

    Article  Google Scholar 

  78. Mendoza-Palechor, F. E., Ariza-Colpas, P. P., Sepulveda-Ojeda, J. A., De-la-Hoz-Manotas, A., & Piñeres Melo, M. (2016). Fertility analysis method based on supervised and unsupervised data mining techniques.

  79. Candemir, C. (2018). Estimating the semen quality from life style using fuzzy radial basis functions. Int J Mach Learn Comput, 8(1), 44-8.

    Article  Google Scholar 

  80. Gil, D., Girela, J. L., De Juan, J., Gomez-Torres, M. J., & Johnsson, M. (2012). Predicting seminal quality with artificial intelligence methods. Expert Systems with Applications, 39(16), 12564-12573.

    Article  Google Scholar 

  81. Yibre, A. M., & Koçer, B. (2021). Semen quality predictive model using Feed Forwarded Neural Network trained by Learning-Based Artificial Algae Algorithm. Engineering Science and Technology, an International Journal, 24(2), 310-318.

    Article  Google Scholar 

  82. Ma, J., Afolabi, D. O., Ren, J., & Zhen, A. (2019). Predicting seminal quality via imbalanced learning with evolutionary safe-level synthetic minority over-sampling technique. Cognitive Computation, 1–12.

  83. Dash, S. R., & Ray, R. (2020). Predicting seminal quality and its dependence on life style factors through ensemble learning. International Journal of E-Health and Medical Communications (IJEHMC), 11(2), 78-95.

    Google Scholar 

  84. Rhemimet, A., Raghay, S., & Bencharef, O. (2016). Comparative Analysis of Classification, Clustering and Regression Techniques to Explore Men’s Fertility. In Proceedings of the Mediterranean Conference on Information & Communication Technologies 2015 (pp. 455–462). Springer, Cham.

  85. Roy, D. G., & Alvi, P. A. (2022). Detection of Male Fertility Using AI-Driven Tools. In International Conference on Recent Trends in Image Processing and Pattern Recognition (pp. 14–25). Springer, Cham.

  86. Riordon, J., McCallum, C., & Sinton, D. (2019). Deep learning for the classification of human sperm. Computers in biology and medicine, 111, 103342.

    Article  PubMed  Google Scholar 

  87. Iqbal, I., Mustafa, G., & Ma, J. (2020). Deep learning-based morphological classification of human sperm heads. Diagnostics, 10(5), 325.

    Article  PubMed  PubMed Central  Google Scholar 

  88. Tsai, V. F., Zhuang, B., Pong, Y. H., Hsieh, J. T., & Chang, H. C. (2020). Web-and Artificial Intelligence–Based Image Recognition for Sperm Motility Analysis: Verification Study. JMIR medical informatics, 8(11), e20031.

    Article  PubMed  PubMed Central  Google Scholar 

  89. Lesani, A., Kazemnejad, S., Zand, M. M., Azadi, M., Jafari, H., Mofrad, M. R., & Nosrati, R. (2020). Quantification of human sperm concentration using machine learning-based spectrophotometry. Computers in Biology and Medicine, 127, 104061.

    Article  CAS  PubMed  Google Scholar 

  90. Hicks, S. A., Andersen, J. M., Witczak, O., Thambawita, V., Halvorsen, P., Hammer, H. L., ... & Riegler, M. A. (2019). Machine learning-based analysis of sperm videos and participant data for male fertility prediction. Scientific reports, 9(1), 1-10.

    Article  Google Scholar 

  91. Agarwal, A., Henkel, R., Huang, C. C., & Lee, M. S. (2019). Automation of human semen analysis using a novel artificial intelligence optical microscopic technology. Andrologia, 51(11), e13440.

    Article  PubMed  Google Scholar 

  92. Ghasemian, F., Mirroshandel, S. A., Monji-Azad, S., Azarnia, M., & Zahiri, Z. (2015). An efficient method for automatic morphological abnormality detection from human sperm images. Computer methods and programs in biomedicine, 122(3), 409-420.

    Article  PubMed  Google Scholar 

  93. Hicks, S., Halvorsen, P., Haugen, T. B., Andersen, J. M., Witczak, O., Hammer, H. L., ... & Riegler, M. (2019). Predicting Sperm Motility and Morphology using Deep Learning and Handcrafted Features.

  94. Somasundaram, D., & Nirmala, M. (2021). Faster region convolutional neural network and semen tracking algorithm for sperm analysis. Computer Methods and Programs in Biomedicine, 200, 105918.

    Article  PubMed  Google Scholar 

  95. Ademola, b. J., adebayo, i. P., & thomas, b. O. A predictive model for the risk of infertility in women using supervised machine learning algorithms (a comparative analysis).

  96. Simi, M. S., Nayaki, K. S., Parameswaran, M., & Sivadasan, S. (2017, October). Exploring female infertility using predictive analytic. In 2017 IEEE Global Humanitarian Technology Conference (GHTC) (pp. 1–6). IEEE.

  97. Aror, A., & Chouksey, P. (2017). A novel approach for women's infertility detection using data mining techniques. Int J Electron Commun Comput Eng, 8, 129-33

    Google Scholar 

  98. Asmy Denny, Anita Raj, Ashi Ashok, C. Maneesh Ram, Remya George, “i-HOPE: Detection and Prediction System for Polycystic Ovary Syndrome (PCOS) Using Machine Learning Techniques,” TENCON 2019–2019 IEEE Region 10 Conference (TENCON), pp.673–78, 2019.

  99. B. Vikas, Anuhya, BS, Chilla, Manaswini, Sarangi, Sipra, “A Critical Study of Polycystic Ovarian Syndrome (PCOS) Classification Techniques”, International Journal of Computational Enginering & Management, vol. 21, 2018.

  100. Zhang, Xing-Zhong, Pang, Yan-Li, Wang, Xian Li, Yan-Hui, “Computational characterization and identification of human polycystic ovary syndrome genes”, Scientific reports, vol.8, no.1, pp.1-7, 2018

    Google Scholar 

  101. Dewi, RM, Wisesty, UN, “Classification of polycystic ovary based on ultrasound images using competitive neural network,” Journal of Physics Conference Series, vol.971, no.1, pp.0120105, 2018.

    Google Scholar 

  102. Cheng J, Jojo, Mahalingaiah, and Shruthi, “Data mining and classification of polycystic ovaries in pelvic ultrasound reports,” bioRxiv, pp. 254870, 2018.

  103. K. Meena, Dr. M. Manimekalai, S. Rethinavalli, “Correlation of Artificial Neural Network Classification and NFRS Attribute Filtering Algorithm for PCOS Data”, Int. J. Res. Eng. Technol, vol.5, no.3, pp. 519–24, 2015.

  104. Joham, A.E., Teede, H.J., Ranasinha, S., Zoungas, S. and Boyle, J., “Prevalence of infertility and use of fertility treatment in women with polycystic ovary syndrome: data from a large community-based cohort study,” Journal of women's health, vol. 24, no. 4, pp. 299-307, 2015.

    Article  PubMed  Google Scholar 

  105. Thomas, N. A review on prognosis of pcos using nfrs, hybrid technique and chi-square test. In Proceedings of National Conference on Advanced Computing and Communication Technology ISBN (Vol. 97893, pp. 52886–869).

  106. Mehrotra, P., Chatterjee, J., Chakraborty, C., Ghoshdastidar, B., & Ghoshdastidar, S. (2011). Automated screening of polycystic ovary syndrome using machine learning techniques, 2011 Annual IEEE India Conference, pp.1–5.

  107. Meena, D. K., Manimekalai, D. M., & Rethinavalli, S. (2015). A novel framework for filtering the PCOS attributes using data mining techniques. International Journal of Engineering Research & Technology, 4(1), 702-706.

    Google Scholar 

  108. Meena, D. K., Manimekalai, D. M., & Rethinavalli, S. (2015). Implementing neural fuzzy rough set and artificial neural network for predicting PCOS. International Journal on Recent and Innovation Trends in Computing and Communication, 3(12), 6722-6727.

    Google Scholar 

  109. Roy, D. G., & Alvi, P. A. (2022). Artificial Intelligence in Diagnosis of Polycystic Ovarian Syndrome. In Contemporary Issues in Communication, Cloud and Big Data Analytics (pp. 453–463). Springer, Singapore.

  110. Cai, J., Luo, J., Wang, S., & Yang, S. (2018). Feature selection in machine learning: A new perspective. Neurocomputing, 300, 70-79.

    Article  Google Scholar 

  111. Piles, M., Bergsma, R., Gianola, D., Gilbert, H., & Tusell, L. (2021). Feature selection stability and accuracy of prediction models for genomic prediction of residual feed intake in pigs using machine learning. Frontiers in genetics, 12, 611506.

    Article  PubMed  PubMed Central  Google Scholar 

  112. Karabulut, E. M., Özel, S. A., & Ibrikci, T. (2012). A comparative study on the effect of feature selection on classification accuracy. Procedia Technology, 1, 323-327.

    Article  Google Scholar 

  113. Chu, C., Hsu, A. L., Chou, K. H., Bandettini, P., Lin, C., & Alzheimer's Disease Neuroimaging Initiative. (2012). Does feature selection improve classification accuracy? Impact of sample size and feature selection on classification using anatomical magnetic resonance images. Neuroimage, 60(1), 59-70.

    Article  PubMed  Google Scholar 

  114. Güvenir, H. A., Misirli, G., Dilbaz, S., Ozdegirmenci, O., Demir, B., & Dilbaz, B. (2015). Estimating the chance of success in IVF treatment using a ranking algorithm. Medical & biological engineering & computing, 53(9), 911-920.

    Article  Google Scholar 

  115. Hassan, M. R., Al-Insaif, S., Hossain, M. I., & Kamruzzaman, J. (2020). A machine learning approach for prediction of pregnancy outcome following IVF treatment. Neural computing and applications, 32(7), 2283-2297.

    Article  Google Scholar 

  116. Liberati, A., Altman, D. G., Tetzlaff, J., Mulrow, C., Gøtzsche, P. C., Ioannidis, J. P., ... & Moher, D. (2009). The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. Journal of clinical epidemiology, 62(10), e1-e34.

  117. Valiuškaitė V, Raudonis V, Maskeliūnas R, Damaševičius R, Krilavičius T. Deep Learning Based Evaluation of Spermatozoid Motility for Artificial Insemination. Sensors (Basel). 2020 Dec 24;21(1):72. doi: https://doi.org/10.3390/s21010072. PMID: 33374461; PMCID: PMC7795243.

    Article  PubMed  PubMed Central  Google Scholar 

  118. GhoshRoy, D., Alvi, P. A., & Santosh, K. C. (2022). Explainable AI to Predict Male Fertility Using Extreme Gradient Boosting Algorithm with SMOTE. Electronics, 12(1), 15.

    Article  Google Scholar 

  119. GhoshRoy, D., Alvi, P. A., & Santosh, K. C. (2023, April). Unboxing Industry-Standard AI Models for Male Fertility Prediction with SHAP. In Healthcare (Vol. 11, No. 7). Multidisciplinary Digital Publishing Institute (MDPI).

Download references

Funding

There was no funding for this study.

Author information

Authors and Affiliations

  1. School of Automation, Banasthali Vidyapith, 304022, Rajasthan, India

    Debasmita GhoshRoy

  2. Department of Physics, Banasthali Vidyapith, 304022, Rajasthan, India

    P. A. Alvi

  3. Department of Computer Science, University of South Dakota, Vermillion, SD, 57069, USA

    KC Santosh

  4. Applied AI Research Lab, Vermillion, SD, 57069, USA

    Debasmita GhoshRoy & KC Santosh

Authors
  1. Debasmita GhoshRoy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. P. A. Alvi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. KC Santosh
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

DGR and KC conceptualized the study and its methodology. DGR wrote the original draft that was reviewed and edited by PA and KC. KC finalized/corrected the revised submission.

Not applicable.

Corresponding author

Correspondence to KC Santosh.

Ethics declarations

Ethical approval and consent to participate

This article does not include any human participant studies conducted by any of the authors. Since this is a review, consent is not required.

Human and animal ethics

This study did not include any human subjects or animals.

Consent for publication

This article contains no identifying information, so it is inapplicable.

Competing interests

There are no potential conflicts of interest reported by any of the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

GhoshRoy, D., Alvi, P.A. & Santosh, K. AI Tools for Assessing Human Fertility Using Risk Factors: A State-of-the-Art Review. J Med Syst 47, 91 (2023). https://doi.org/10.1007/s10916-023-01983-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10916-023-01983-8

Keywords

Search

Navigation