ARCHIVES

Review Article

Artificial Intelligence in Human in Vitro Fertilization

Harpreet Kaur1 Dr. Priyanka Gupta2 Dr. Kamaljit Kaur3
1, 2, 3 PG, Department of Biotechnology, Khalsa College Amritsar, Punjab, India.

Published Online: March-April 2025

Pages: 99-103

Cite this article

No DOI

References

1. Louise Brown: World's first IVF babies family archive unveiled.’’ 24 July 2018. Retrieved 29 July 2021
2. Oktay, K., Harvey, B. E., & Partridge, A. H. (2018). Fertility preservation in patients with cancer: ASCO clinical practice guideline update.
Journal of Clinical Oncology, 36(19), 1994–2001
3. Van der Gaast, M., et al. (2018). Factors influencing the success of IVF treatment: A meta-analysis. Human Reproduction Update, 24(1),
31-45
4. Rich E, Knight K, Nair S. Artificial intelligence. 3rd ed. McGrawHill. 2008.
5. Penixoto MS, Barros LC, Bassanezi RC, Fernandes OA. An approach via fuzzy systems for dynamics and control of the soybean aphid.
Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology.
Atlantic Press. Adv Intel Syst Res. 2015;89:1295–301.
6. Ombelet, W., and Campo, R. (2007). Affordable IVF for developing countries. Reprod. Biomed. Online 15, 257–265.1472-6483(10)60337-9.
7. ESHRE (2023). ART.Factsheet. https://www.eshre.eu/Europe/ Factsheets-and-infographics
8. Pinborg, A., Henningsen, A.-K.A., Malchau, S.S., and Loft, A. (2013). Congenital anomalies after assisted reproductive technology. Fertil.
Steril. 99, 327–332
9. Gosden R (June 2018). "Jean Marian Purdy remembered – the hidden life of an IVF pioneer". Human Fertility. 21 (2): 86–89. doi:
10.1080/14647273.1351042. PMID 28881151. S2CID 5045457
10. Reuters. 3 May 2012. Retrieved 5 November 2015.2008;38:1177–86.
11. “ Fertility: assessment and treatment for people with fertility problems’’ . NICE clinical guideline. February 2013
12. Wang, J., and Sauer, M.V. (2006). In vitro fertilization (IVF): a review of 3 decades of clinical innovation and technological advancement.
Therapeut. Clin. Risk Manag. 2, 355–364
13. Baxter Bendus, A.E., Mayer, J.F., Shipley, S.K., and Catherino, W.H. (2006). Interobserver and intraobserver variation in day 3 embryo
grading. Fertil. Steril. 86, 1608–1615.
14. Storr, A., Venetis, C.A., Cooke, S., Kilani, S., and Ledger, W. (2017). Interobserver and intra-observer agreement between embryologists
during selection of a single Day 5 embryo for transfer: a multicenter study. Hum. Reprod. 32, 307–314.
15. Wahl, B., Cossy-Gantner, A., Germann, S., and Schwalbe, N.R. (2018). Artificial intelligence (AI) and global health: how can AI contribute to
health in resource-poor settings? BMJ Glob. Health 3, e000798.
16. Hosny, A., and Aerts, H.J.W.L. (2019). Artificial intelligence for global health. Science 366, 955–956.
17. La Marca A, Sunkara SK (2014). Individualization of controlled ovarian stimulation in IVF using ovarian reserve markers: from theory to
practice.” Human Reproduction Update. 20 (1): 124–140.
18. Humaidan P, Kol S, Papanikolaou EG, et al. (Copenhagen GnRH Agonist Triggering Workshop Group) (2011). “ GnRH agonist for
triggering of final oocyte maturation: time for a change of practice?” .Human Reproduction Update. 17 (4): 510–524
19. Zhang XD, Liu JX, Liu WW, Gao Y, Han W, Xiong S, et al. (2013). “Time of insemination culture and outcomes of in vitro fertilization : a
systematic review and meta analysis” . Human Reproduction Update. 19 (6): 685–695.
20. Abyholm T, Tanbo T, Dale PO , Mangus O ( February 1992). “In vivo fertilization procedures in infertile women with patent fallopian tubes:
a comparison of gamete intrafallopian transfer, combined intrauterine and intraperitoneal insemination, and controlled ovarian
hyperstimulation alone” . Journal of assisted Reproduction and Genetics. 9 (1): 19–23.
21. Wetscher F, Havlicek V, Huber T, Gilles M, Tesfaye D, Griese J, et al.(July 2005). “ Intrafallopian transfer of gametes and early stage embryos
for in vivo culture in cattle” . Theriogenology. 64 (1): 30–40.
22. Dar S, Lazer T, Shah PS, Librach CL (2014). “Neonatal outcomes among singleton births after blastocyst versus cleavage stage embryo
transfer: a systematic review and meta-analysis” . Human Reproduction Update. 20 (3): 439–448.
23. Farquhar C, Marjoribanks J ( August 2018). “ Assisted reproduction technology: an overview of Cochrane Reviews”. The Cochrane Database
of Systematic Reviews. 2018 (8): CD010537
24. Timeva T, Shterev A, Kyurkchiev S (october 2014). “Recurrent implantation failure: the role of the endometrium” . Journal of Reproduction
& Infertility. 15 (4): 173–183.
25. Hearns-Stokes, R.M. Miller, B.T. Scott, L. … Pregnancy rates after embryo transfer depend on the provider at embryo transfer Fertil Steril.
2000; 74: 80-86 Level II-2
26. Angelini, A. Brusco, G.F. Barnocchi, N. … Impact of physician performing embryo transfer on pregnancy rates in an assisted reproductive
program. J Assist Reprod Genet. 2006; 23:329-332 Level II-2
27. De Geyter C, Wyns C, Calhaz-Jorge C, de Mouzon J, Ferraretti AP, Kupka M, Nyboe Andersen A, Nygren KG & Goossens V 2020 20 years of
the European IVF- monitoring consortium registry: what have we learned? A comparison with registries from two other regions. Human
Reproduction 35 2832–2849.
28. Go, K.J.. “By the work, one knows the workman’’: the practice and profession of the embryologist and its translation to quality in the
embryology laboratory. Reprod. Biomed. Online. 31(4), 449–458(2015).
9. Zaninovic, N., Elemento, O., Rosenwaks, Z.: Artificial intelligence: its application in reproductive medicine and the assisted reproductive
technologies. Fertil. Steril. 112(1), 28–30(2019).
30. Fernandez, E.I., Ferreira, A.S., Cecilio, M.H.M., et al.: Artificial intelligence in the IVF laboratory: overview through the application of
different types of algorithms for the classification of reproductive data. J. Assist. Reprod. Genet. 37, 2359–2376.
31. Hajirasouliha, I., Elemento, O.: Precision medicine and artificial intelligence : overview and relevance to reproductive medicine. Fertil. Steril.
114(5), 908–913(2020).
32. Chow, D.J.X., Wijesinghe, P., Dholakia, K., Dunning, K.R.: Does artificial intelligence have a role in the IVF clinic? Reprod. Fertil. 2(3),
C29–C34 (2021).
33. Goyal, A., Kuchana, M., Ayyagari, K.P.R.: Machine learning predicts live birth occurrence before in-vitro fertilization treatment. Sci. Rep. 10,
20925(2020).
34. Gunderson, S.J., Puga Molina, L.S., Spies, N., Balestrini, P.A., et al.: Machine- learning algorithm incorporating capacitated sperm
intracellular pH predicts conventional in vitro fertilization success in normospermic patients. Fertil. Steril. 115(4), 930–938(2021)
35. Jatin, K.: Artificial intelligence in assisted reproductive technology– current scenario and future implications . Fertil. Sci. Res. 6(2), 57–60
(2019).
36. Manna C, Nanni L, Lumini A& Pappalardo S 2013 Artificial intelligence technique for embryo and oocyte classification. Reproductive
Biomedicine Online 26 42–49.
37. Gianaroli L, Magli MC, Gambardella L, Giusti A, Grugnetti C, Corani G. Objective way to support embryo transfer: a probabilistic decision.
Hum Reprod. 2013; 28:1210–20.
38. Hernandez-Gonzalez J, Inza I, Crisol-Ortiz L, Guembe MA, Inarra MJ, Lozano JA. Fitting the data from embryo implantation prediction:
learning from label proportions. Stat methods Med Res. 2018;27:1056–66.
39. Morales DA, Bengoetxea E, Larranaga P. Selection of human embryos for transfer by Bayesian classifiers. Com Biol Med. 2008;38:1177–86.
40. Corani G, Magli C, Giusti A, Gianaroli L, Gambardella LM. A Bayesian network model for predicting pregnancy after in vitro fertilization.
Comput Biol Med.2013
41. Morales DA, Bengoetxea E, Larranaga P, Garcia M, Franco Y, Fresnada M, et al. Bayesian classification for the selection of in vitro human
embryos using morphological and clinical data. Comput methods prog Biomed. 2008;90:104–16.
42. Raudonis, V., Paulauskaite-Taraseviciene, A., Sutiene, K., etal.:Towards the automation of early-stage human embryo development
detection. BioMed. Eng. OnLine 18,120 (2019)
43. Khosravi, P., Kazemi, E., Zhan, Q., et al.: Deep learning enables robust assessment and selection of human blastocysts after in vitro
fertilization. Digit. Med. 2,21 (2019)
44. Ducharme, J.: how artificial intelligence could change the fertility world. Time (29 January 2019). Accessed 9 March 2022.
45. Inhorn, M. C. & Patrizio, P. infertility around the globe: new thinking on gender, reproductive technologies and global movements in the 21st
century. Hum. Reprod. Update 21, 411–426 (2015).
46. Agarwal, A., Mulgund, A., Hamada, A. & Chyatte, M. R. A unique view on male infertility around the globe. Reprod . Biol. Endocrinol. 13, 37
(2015).
47. Ombelet W, Global access to infertility care in developing countries: a case of human rights, equity and social justice. Facts Views Vis Obgyn
2011;3: 257–66.
48. Bjorndahl L, Kirkman Brown J. Other Editorial Board Members of the WHO Laboratory Manual for the Examination and Processing of
Human Semen. The sixth edition of the WHO laboratory Manual for the examination and processing of Human semen: ensuring quality and
standardization in basic examination of human ejaculates . Fertil Steril 2022; 117: 246–51.
49. Donnelly ET, Lewis SE, McNally JA, Thompson W. In vitro fertilization and pregnancy rates: the influence of sperm motility and morphology
on IVF outcome. Fertil Steril 1998;70:305–14.
50. Tsai, V.F. Zhuang, B. Pong, Y.H. … Web- and artificial intelligence- based recognition for sperm motility analysis: verification study JMIR
Med Inform. 2020; 8. e20031
51. Ottl, S. Amiriparian, S. Gerczuk, M. … motilityAI: a machine learning framework for automatic prediction of human sperm motility iscience.
2022; 25, 104644.
52. Chow, D.J.X., Wijesinghe, P., Dholakia, K., Dunning, K.R.: Does artificial intelligence have a role in the IVF clinic? Reprod. Fertil. 2(3),
C29–C34 (2021)
53. Heitman, E.: Social and ethical aspects of in vitro fertilization. Int. J. Technol. Assess. Health. Care. 15(1), 22–35 (1999)
54. Khosravi P, Kazemi E, Zhan Q, Malmsten JE, Toschi M, Zisimopoulos P, Sigaras A, Lavery S, Cooper LAD, Hickman C et al. Deep learning
enables robust assessment and selection of human blastocysts after in vitro fertilization. NPJ Digit Med 2019;2:21

Related Articles

2025

A Comprehensive Review on Antibiotic Resistance

2025

AI-Driven Conversational Models for Supporting Migrant Career Guidance and Labour Market Integration: A Scoping Review

2025

Cloud-Based MIS Framework for Streamlining Outcome-Based Education Evaluation in Higher Education

2025

A Scalable System Design for Real-Time Personalized Recommendation Engines in E-Commerce

2025

AI-Powered Career Advisor (A Personalized Career Guidance System)

2025

Web News Pulse: Smart Web Scraping Based News Platform

Share Article

X
LinkedIn
Facebook
WhatsApp

Or copy link

https://test.ijsreat.com/archives/artificial-intelligence-in-human-in-vitro-fertilization

*Instagram doesn't support direct link sharing from web. Copy the link and share it in your Instagram story or post.