Women Infertility: A Systematic Review of Effects and Causes

Recently, infertility has been affecting a large number of women than men. It is the male or female reproductive system’s disease. Consequently, after 12 months or more of usual insecure sexual intercourse, if they fail to attain pregnancy then that failure is defined as infertility. As per the National Institute of Child Health and Human Development, people in the USA have infertility issues that are found in 11% of women along with 9% of men. Women are more fertile in their 20s, while in their 30s it reduces to half of it. After 35 years, the probability of getting pregnant is diminished in women. For the woman to get pregnant, the proper functioning of the ovaries, fallopian tubes, and uterus is required. So, infertility occurs due to any issues with the above body parts. Women's infertility is caused by many factors, but the most vital cause is polycystic ovary syndrome (PCOS), which is a hormonal disorder commonly found among reproductive-aged women. So, women infertility, effects of women infertility, causes of women infertility, PCOS and detection of PCOS by general and machine learning (ML) techniques had been discussed in this paper. The accuracy attained in the detection of PCOS utilizing ML techniques is analyzed. Among people as of the top five responding countries that incorporate the USA, UK, Australia, India, together with the Philippines, the percentage of women with physician-assured PCOS against women exclusive of PCOS is analyzed.

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References

  1. Melodie Vander B, Christineyns W. Fertility and infertility definition and epidemiology. Clin Biochem. 2018;62:2–10. Google Scholar
  2. American Society for Reproductive Medicine. Infertility an overview a guide for patients. 2017. https://www.reproductivefacts.org/globalassets/rf/news-and-publications/bookletsfact-sheets/english-fact-sheets-and-info-booklets/infertility-an_overview_booklet2.pdf.
  3. Mara M, Laura C, Manuela U, Emanuele P. Editorial female infertility genetics of reproductive ageing, menopause and primary ovarian insufficiency. Front Genet. 2022;13:1–3. Google Scholar
  4. Narjes D, Tina M, Meimanat H. Infertility related risk factors a systematic review. Int J Women’s Health Reprod Sci. 2017;5(1):24–9. Google Scholar
  5. Shahnaz A, Ayesha A. Infertility a review on causes, treatment and management. Women’s Health Gynecol. 2016;2(6):1–5. Google Scholar
  6. Murtaza M, Sharifa AM, Janan H, Iiizam EM, Aliya S. Male and female infertility causes, and management. IOSR J Dent Med Sci. 2019;18(9):27–32. Google Scholar
  7. Female Infertility. Cleveland Clinic, 2020, https://my.clevelandclinic.org/health/diseases/17774-female-infertility.
  8. ZeynepOzcan D, Berna D. Impact of obesity on infertility in women. J Turk German Gynecol Assoc. 2015;16(2):111–7. Google Scholar
  9. Female infertility. Mayo Clinic, 2021, https://www.mayoclinic.org/diseases-conditions/female-infertility/symptoms-causes/syc-20354308.
  10. M. Ashraf Direkvand, D. Ali and A. Khosravi. Epidemiology of female infertility a review of literature. Biosciences.
  11. Barbieri RL. Female infertility. 8th ed. Elsevier; 2019. (ISBN: 978-0-323-47912-7). Google Scholar
  12. Nardo LG, Chouliaras S. Definitions and epidemiology of unexplained female infertility. Obstet Gynecol Surv. 2015;69(2):109–15. Google Scholar
  13. Ogawa M, Takamatsu K, Horiguchi F. Evaluation of factors associated with the anxiety and depression of female infertility patients. BioPsychoSocial Med. 2011;5:1–5. Google Scholar
  14. Simi MS, Sankara Nayaki K, Parameswaran M, Sivadasan S. Exploring female infertility using predictive analytic. In: Global Humanitarian Technology Conference, IEEE, 9–22 October 2017, San Jose, CA, USA 2017.
  15. Brazdova A, Senechal H, Peltre G, Poncet P. Immune aspects of female infertility. Int J Fertil Steril. 2016;10(1):1–10. Google Scholar
  16. Mori K, Kitaya K, Ishikawa T, Hata Y. Analysis of endometrium form by using l bp for female infertility. In: International Conference on Machine Learning and Cybernetics, 15–18 July 2018, Chengdu, China, 2018.
  17. Nishihara R, Matsubayashi H, Ishikawa T, Hata H. Automated diagnosis of the frequency of uterine peristalsis for female infertility. In: 50th International Symposium on Multiple-Valued Logic, IEEE, 09–11 November 2020, Miyazaki, Japan, 2020.
  18. Kabadi YM, Harsha B. Hysterolaparoscopy in the evaluation and management of female infertility. J Obstet Gynecol India. 2016;66:478–81. Google Scholar
  19. Moghadam AD, Delpisheh A, Direkvand-Moghadam A. Effect of infertility on sexual function a cross-sectional study. J Clin Diagn Res. 2015;9(5):1–3. Google Scholar
  20. Salomao PB, Navarro PA, Romao APMS, Lerri MR, da Silva Lara LA. Sexual function of women with infertility. Revista Brasileira de Ginecologiae Obstetrícia. 2018;40(12):771–8. Google Scholar
  21. Webair HH, Ismail TAT, Ismail SB, et al. Patient-centered infertility questionnaire for female clients (PCIQ-F) part I questionnaire development. BMC Med Res Methodol. 2021;21(1):1–10. Google Scholar
  22. Kashi AM, Moradi Y, Chaichian S, Najmi Z, Mansori K, Salehin F, Rastgar A, Khateri S. Application of the World Health Organization Quality of Life Instrument, Short Form (WHOQOL-BREF) to patients with endometriosis. Obstet Gynecol Sci. 2018;61(5):598–604. Google Scholar
  23. Aimagambetova G, Issanov A, Terzic S, Bapayeva G, Ukybassova T, Baikoshkarova S, Aldiyarova A, Shauyen F, Terzic M. The effect of psychological distress on IVF outcomes reality or speculations. PLoS ONE. 2020;15(12):1–14. Google Scholar
  24. Wdowiak A, Anusiewicz A, Bakalczuk G, Raczkiewicz D, Janczyk P, Makara-Studzinska M. Assessment of quality of life in infertility treated women in Poland. Int J Environ Res Public Health. 2021;18(8):1–13. Google Scholar
  25. Maroufizadeh S, Riazi H, Lotfollahi H, Omani-Samani R, Amini P. The 6-item Female Sexual Function Index (FSFI-6) factor structure, reliability, and demographic correlates among infertile women in Iran. Middle East Fertil Soc J. 2019;24:1–6. Google Scholar
  26. Al-Homaidan HT. Depression among women with primary infertility attending an infertility clinic in Riyadh, Kingdom of Saudi Arabia rate, severity, and contributing factors. Int J Health Sci Qassim Univ. 2011;5(2):108–15. Google Scholar
  27. Bakhtiyar K, Beiranvand R, Ardalan A, Changaee F, Almasian M, Badrizadeh A, Bastami F, Ebrahimzadeh F. An investigation of the effects of infertility on women’s quality of life a case–control study. BMC Womens Health. 2019;19:1–9. Google Scholar
  28. What are some possible causes of female infertility. 2017, https://www.nichd.nih.gov/health/topics/infertility/conditioninfo/causes/causes-female
  29. Amudha M, Rani S, Kannan K, Manavalan R. An updated overview on causes, diagnosis and management of infertility. Int J Pharm Sci Rev Res. 2013;18(1):155–64. Google Scholar
  30. Hernandez-Angeles C, Castelo-Branco C. Early menopause a hazard to a woman’s health. Indian J Med Res. 2016;143(4):420–7. Google Scholar
  31. Mafra FA, Christofolini DM, Cavalcanti V, Vilarino FL, Andre GM, Kato P, Bianco B, Barbosa CP. Aberrant telomerase expression in the endometrium of infertile women with deep endometriosis. Arch Med Res. 2014;45:31–5. Google Scholar
  32. Carranza-Mamane B, Havelock J, Hemmings R. The management of uterine fibroids in women with otherwise unexplained infertility. Pract Guidel. 2015;37(3):277–85. Google Scholar
  33. Cook AS, David-Adamson G. The role of the endometriosis fertility index (EFI) and endometriosis scoring systems in predicting infertility outcomes. Curr Obstet Gynecol Rep. 2013;2(3):186–94. Google Scholar
  34. Koninckx PR, Ussia A, Keckstein J, Adamyan L, Wattiez A, Martin DC. Prevalence of endometriosis and peritoneal pockets in women with infertility and/or pelvic pain. Gynaecol Gynaecol. 2021;43(8):935–42. Google Scholar
  35. Santoso B, Rahmawati NY, Saadi A, Dwiningsih SR, Annas JY, Tunjungseto A, Ardianta-Widyanugraha MY, Mufid AF, Ahsan F. Elevated peritoneal soluble endoglin and GDF-15 in infertile women with severe endometriosis and pelvic adhesion. J Reprod Immunol. 2021;146:1–10. Google Scholar
  36. Yasui T, Hayashi K, Mizunuma H, Kubota T, Asod T, Matsumura Y, Lee JS, Suzuki S. Association of endometriosis-related infertility with age at menopause. Maturitas. 2011;69:279–83. Google Scholar
  37. Huang H, Kuang H, Sun F, et al. Lower prevalence of non-cavity distorting uterine fibroids in patients with polycystic ovary syndrome than in those with unexplained infertility. Fertil Steril. 2019;111(5):1011–9. Google Scholar
  38. Huang S, Du X, Wang R, Li R, Wang H, Luo L, O’Leary S, Qiao J, Mol BWJ. Ovulation induction and intrauterine insemination in infertile women with polycystic ovary syndrome a comparison of drugs. EURO. 2018;231:117–21. Google Scholar
  39. Vannuccini S, Clifton VL, Fraser IS, Taylor HS, Critchley H, Giudice LC, Petraglia F. Infertility and reproductive disorders impact of hormonal and inflammatory mechanisms on pregnancy out-come. Hum Reprod Update. 2016;22(1):104–15. Google Scholar
  40. Yang X-J. Telocytes in inflammatory gynaecologic diseases and infertility. Adv Exp Med Biol. 2016;913:263–85. Google Scholar
  41. Dennett CC, Simon J. The role of polycystic ovary syndrome in reproductive and metabolic health over- view and approaches for treatment. Diabetes Spectr. 2015;28(2):116–20. Google Scholar
  42. Mondal S. Polycystic ovary syndrome. Lecture Notes, 2020, https://doi.org/10.13140/RG.2.2.22872.03840.
  43. Mahoney D. Lifestyle modification intervention among infertile overweight and obese women with polycystic ovary syndrome. J Am Assoc Nurse Pract. 2014;26:301–8. Google Scholar
  44. Kumar AN, Naidu JN, Satyanarayana U, Ramalingam K, Anitha M. Metabolic and endocrine characteristics of Indian women with polycystic ovary syndrome. Int J Fertil Steril. 2016;10(1):22–8. Google Scholar
  45. Wright PJ, Dawson RM, Corbett CF. Social construction of biopsychosocial and medical experiences of women with polycystic ovary syndrome. J Adv Nurs. 2020;76:1728–36. Google Scholar
  46. Neagu M, Cristescu C. Anti-Mullerian hormone a prognostic marker for metformin therapy efficiency in the treatment of women with infertility and polycystic ovary syndrome. J Med Life. 2012;5(4):462–4. Google Scholar
  47. Tayrab E, Ali M, Modawe GA, Naway L, Abdrabo AEA. Serum Anti-Müllerian hormone as laboratory predictor in infertile women with and without polycystic ovary syndrome. Am J Res Commun. 2014;2(3):61–6. Google Scholar
  48. Wang C, Wei Wu, Yang H, Ye Z, Zhao Y, Liu J, Liangshan Mu. Mendelian randomization analyses for PCOS: evidence, opportunities and challenges. Trends Genet. 2022;38(5):468–82. Google Scholar
  49. Fetouh AA, Mohamed RS. Ovarian Doppler study in polycystic ovary syndrome in relation to body weight. Al-AzharAssiut Med J. 2015;13(3):34–42. Google Scholar
  50. Lujan ME, Jarrett BY, Brooks ED, Reines JK, Peppin AK, Muhn N, Haider E, Pierson RA, Chizen DR. Updated ultrasound criteria for polycystic ovary syndrome reliable thresholds for elevated follicle population and ovarian volume. Hum Reprod. 2013;28(5):1361–8. Google Scholar
  51. Lee TT, Rausch ME. Polycystic Ovarian Syndrome: role of imaging in diagnosis. Radio Graph. 2012;32(6):1643–57. Google Scholar
  52. Rachana B, Priyanka T, Sahana KN, Supritha TR, Parameshachari DB, Sunitha R. Detection of polycystic ovarian syndrome using follicle recognition technique. Glob Trans Proc. 2021;2(2):304–8. Google Scholar
  53. Ali HI, Elsadawy ME, Khater NH. Ultrasound assessment of polycystic ovaries Ovarian volume and morphology; which is more accurate in making the diagnosis. Egypt J Radiol Nucl Med. 2016;47:347–50. Google Scholar
  54. Tsymbal S. 5 essential machine learning algorithms for business applications. 2020, https://mobidev.biz/blog/5-essential- machine-learning-techniques.
  55. Garg S, Wadi MKS, Garg B. Color Doppler as diagnostic criteria in polycystic ovarian syndrome. Int J Clin Obstet Gynaecol. 2021;5(2):8–11. Google Scholar
  56. Mehr HD, Polat H. Diagnosis of poly- cystic ovary syndrome through different machine learning and feature selection techniques. Health Technol. 2021;12(1):137–50. Google Scholar
  57. Hassan MM, Mirza T. Comparative analysis of machine learning algorithms in diagnosis of polycystic ovarian syndrome. Int J Comput Appl. 2020;175(17):42–53. Google Scholar
  58. Renju K, Pavithra B. Comparative analysis of classifiers for predicting polycystic ovary syndrome using deep learning models. J Emerg Technol Innov Res. 2022;9(6):291–7. Google Scholar
  59. Thakre V, Vedpathak S, Thakre K, Sonawani S. PCOcare PCOS detection and prediction using machine learning algorithms. Biosci Biotechnol Res Commun. 2020;13(14):240–4. Google Scholar
  60. Vikas B, Anuhya BS, Chilla M, Sarangi S. A critical study of polycystic ovarian syndrome (PCOS) classification techniques. Int J Comput Eng Manag. 2018;21(4):1–7. Google Scholar
  61. Purnama B, Wisesti UN, Adiwijaya, Nhita F, Gayatri A, Mutiah T. A classification of polycystic ovary syndrome based on follicle detection of ultra- sound images. In: 3rd International Conference on Information and Communication Technology, 27–29 May 2015, Nusa Dua, Bali, Indonesia, 2015.
  62. Tanwani N. Detecting PCOS using machine learning. Int J Modern Trends Eng Sci. 2020;7(1):1–7. Google Scholar
  63. Mehrotra P, Chatterjee J, Chakraborty C, Ghoshdastidar B, Ghoshdastidar S. Automated screening of polycystic ovary syndrome using machine learning techniques. In: Annual IEEE India Conference, 16–18 December 2011, Hyderabad, India, 2011.
  64. Dutta P, Paul S, Majumder M. An efficient smote based machine learning classification for prediction & detection of PCOS. 2021, https://doi.org/10.21203/rs.3.rs-1043852/v1.
  65. Nilofer NS, Ramkumar R. Follicles classification to detect polycystic ovary syndrome using GLCM and novel hybrid machine learning. Turk J Comput Math Educ. 2021;12(7):1062–73. Google Scholar
  66. Denny A, Raj A, Ashok A, Maneesh Ram C, George R. i-HOPE detection and prediction system for polycystic ovary syndrome (PCOS) using machine learning techniques. In: TENCON 2019—2019 IEEE Region 10 Conference, 17–20 October 2019 Kochi, India, 2019.
  67. Jain T, Negris O, Brown D, Galic I, Salimgaraev R, Zhaunova L. Characterization of polycystic ovary syndrome among Floapp users around the world. Reprod Biol Endocrinol. 2021;19:1–11. Google Scholar

Funding

No funding was received for this research work.