Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

Keynote webinar | Spotlight on medication adherence

LIVE: Thursday 27th June 2024, 18:00-19:30 (CEST)
Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.
ADVANCED SEARCH
Log in
Top
Journal of Medical Systems
Published in: Journal of Medical Systems 1/2023

01-12-2023 | Fertility | Review

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

Authors: Debasmita GhoshRoy, P. A. Alvi, KC Santosh

Published in: Journal of Medical Systems | Issue 1/2023

Login to get access

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.
Literature
1.
Taylor, A. (2003). ABC of subfertility: extent of the problem. BMJ: British Medical Journal, 327(7412), 434
2.
3.
Menken, J., Trussell, J., & Larsen, U. (1986). Age and infertility. Science, 233(4771), 1389-1394.PubMedCrossRef
4.
Sharpe, R. M., & Franks, S. (2002). Environment, lifestyle and infertility—an inter-generational issue. Nature Medicine, 8(10), S33-S40.CrossRef
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.PubMedCrossRef
6.
Bala, R., Singh, V., Rajender, S., & Singh, K. (2021). Environment, lifestyle, and female infertility. Reproductive sciences, 28(3), 617-638.PubMedCrossRef
7.
Unuane, D., Tournaye, H., Velkeniers, B., & Poppe, K. (2011). Endocrine disorders & female infertility. Best Practice & Research Clinical Endocrinology & Metabolism, 25(6), 861-873.CrossRef
8.
Denning, C. R., Sommers, S. C., & Quigley Jr, H. J. (1968). Infertility in male patients with cystic fibrosis. Pediatrics, 41(1), 7-17.PubMedCrossRef
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.PubMedCrossRef
11.
Islam, M., & Rahaman, A. (2020). Development of smart healthcare monitoring system in IoT environment. SN computer science, 1(3), 1-11.CrossRef
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.CrossRef
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.PubMedCrossRef
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.PubMedPubMedCentralCrossRef
16.
Raimundo, J. M., & Cabrita, P. (2021). Artificial intelligence at assisted reproductive technology. Procedia Computer Science, 181, 442-447.CrossRef
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.CrossRef
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.CrossRef
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.PubMedPubMedCentralCrossRef
21.
Larsen, U. (2005). Research on infertility: which definition should we use. Fertility and sterility, 83(4), 846-852.PubMedCrossRef
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.CrossRef
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.PubMedPubMedCentralCrossRef
25.
Lue, T. F. (2000). Erectile dysfunction. New England journal of medicine, 342(24), 1802-1813.PubMedCrossRef
26.
Kubin, M., Wagner, G., & Fugl-Meyer, A. R. (2003). Epidemiology of erectile dysfunction. International Journal of Impotence Research, 15(1), 63-71.PubMedCrossRef
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.PubMedPubMedCentralCrossRef
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.CrossRef
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.PubMedCrossRef
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.PubMedPubMedCentralCrossRef
31.
Wheeler, J. M. (1989). Epidemiology of endometriosis-associated infertility. The Journal of reproductive medicine, 34(1), 41-46PubMed
32.
Balen, A. H., & Rutherford, A. J. (2007). Managing anovulatory infertility and polycystic ovary syndrome. BMJ (Clinical research ed.), 335(7621), 663–666.PubMedCrossRef
33.
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.CrossRef
35.
Kahn, B. E., & Brannigan, R. E. (2017). Obesity and male infertility. Current Opinion in Urology, 27(5), 441-445.PubMedCrossRef
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.CrossRef
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.PubMedPubMedCentralCrossRef
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.PubMedCrossRef
41.
Evenson, D. P., & Wixon, R. (2006). Clinical aspects of sperm DNA fragmentation detection and male infertility. Theriogenology, 65(5), 979-991.PubMedCrossRef
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.PubMedPubMedCentralCrossRef
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.PubMedPubMedCentralCrossRef
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.PubMedPubMedCentralCrossRef
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.PubMedCrossRef
47.
Lotti, F., & Maggi, M. (2018). Sexual dysfunction and male infertility. Nature Reviews Urology, 15(5), 287-307.PubMedCrossRef
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.PubMedPubMedCentralCrossRef
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.CrossRef
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.PubMedCrossRef
51.
Roe, A. H., & Dokras, A. (2011). The diagnosis of polycystic ovary syndrome in adolescents. Reviews in obstetrics & gynecology, 4(2), 45–51.
52.
Al Chami, A., & Saridogan, E. (2017). Endometrial Polyps and Subfertility. Journal of obstetrics and gynaecology of India, 67(1), 9–14.PubMedCrossRef
53.
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
55.
Bulletti, C., Coccia, M. E., Battistoni, S., & Borini, A. (2010). Endometriosis and infertility. Journal of assisted reproduction and genetics, 27(8), 441–447.PubMedPubMedCentralCrossRef
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.PubMedCrossRef
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.PubMed
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.CrossRef
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.PubMedCrossRef
60.
Westrom, L. V. (1996). Chlamydia and its effect on reproduction. Journal of the British Fertility Society, 1(1), 23-30.PubMed
61.
Stoop, D., Cobo, A., & Silber, S. (2014). Fertility preservation for age-related fertility decline. The Lancet, 384(9950), 1311-1319.CrossRef
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.CrossRef
63.
Speroff, L. (1994). The effect of aging on fertility. Current opinion in obstetrics & gynecology, 6(2), 115-120.CrossRef
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.PubMedPubMedCentralCrossRef
66.
Kelly-Weeder, S., & Cox, C. L. (2007). The impact of lifestyle risk factors on female infertility. Women & Health, 44(4), 1-23.CrossRef
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.PubMedCrossRef
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.PubMedCrossRef
69.
Alejandro Oliva, Alfred Spira, Luc Multigner. (2001) Contribution of environmental factors to the risk of male infertility, Human Reproduction, 16 (8), 1768–1776.PubMedCrossRef
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.PubMedPubMedCentralCrossRef
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.PubMedCrossRef
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.
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.PubMedCrossRef
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.CrossRef
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.CrossRef
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.CrossRef
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.CrossRef
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.
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.PubMedCrossRef
87.
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.PubMedPubMedCentralCrossRef
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.PubMedCrossRef
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.CrossRef
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.PubMedCrossRef
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.PubMedCrossRef
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.PubMedCrossRef
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
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
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.
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.PubMedCrossRef
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.
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.
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.CrossRef
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.PubMedPubMedCentralCrossRef
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.CrossRef
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.PubMedCrossRef
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.CrossRef
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.CrossRef
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.
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.CrossRef
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).
Metadata
Title
AI Tools for Assessing Human Fertility Using Risk Factors: A State-of-the-Art Review
Authors
Debasmita GhoshRoy
P. A. Alvi
KC Santosh
Publication date
01-12-2023
Publisher
Springer US
Published in
Journal of Medical Systems / Issue 1/2023
Print ISSN: 0148-5598
Electronic ISSN: 1573-689X
DOI
https://doi.org/10.1007/s10916-023-01983-8

Other articles of this Issue 1/2023

Journal of Medical Systems 1/2023 Go to the issue

Original Paper

Objective Image Quality Comparison Between Brain-Dedicated PET and PET/CT Scanners

Comment

COVID-19 Close Contact Management: An Evolution of Operations Harnessing the Digital Edge

Review

Does Legislation Impede Data Sharing in Australia Across Institutions and Jurisdictions? A Scoping Review

Original Paper

The Effect of Mandala Activity and Technology-Based Breastfeeding Program on Breastfeeding Self-Efficacy and Mother-Infant Attachment of Primiparous Women: A Randomized Controlled Study

Review

Systematic Review of Machine Learning applied to the Prediction of Obesity and Overweight

Original Paper

“ChatGPT, Can You Help Me Save My Child’s Life?” - Diagnostic Accuracy and Supportive Capabilities to Lay Rescuers by ChatGPT in Prehospital Basic Life Support and Paediatric Advanced Life Support Cases – An In-silico Analysis