Artificial Intelligence in Assisted Reproductive Technology

Öz

Artificial Intelligence (AI) has gained significant importance in biomedical fields in recent years, particularly in Assisted Reproductive Technology (ART). ART refers to the methods used in infertility treatment, and the integration of AI in this field holds great potential for optimizing processes. The use of AI has led to significant improvements in critical stages such as sperm analysis, oocyte quality assessment, and embryo selection. AI enables more precise and accurate management of these processes while facilitating the implementation of personalized treatment approaches. AI-assisted systems can increase success rates in infertility treatment, reduce costs, and improve clinical outcomes. It is believed that the integration of AI in ART could contribute to the development of more efficient and effective treatment processes in the future.

Anahtar Kelimeler

• artificial ıntelligence
• assisted reproductive techniques
• Embryology

Yardımcı Üreme Tekniklerinde Yapay Zeka

Öz

Yapay zeka (YZ), son yıllarda biyomedikal alanlarda, özellikle de yardımcı üreme teknikleri (YÜT) içinde önemli bir yer edinmiştir. YÜT, infertilite tedavisinde kullanılan yöntemleri kapsar ve süreçlerin optimize edilmesi için YZ' nin entegrasyonu büyük bir potansiyele sahiptir. YZ kullanımı, sperm analizi, oosit kalitesinin değerlendirilmesi ve embriyo seçimi gibi kritik aşamalarda önemli iyileştirmeler sağlamaktadır. Ayrıca, bu süreçlerin daha hassas ve doğru bir şekilde yönetilmesine olanak tanırken, kişiselleştirilmiş tedavi yaklaşımlarının uygulanmasını da kolaylaştırır. YZ destekli sistemler, infertilite tedavisinde başarı oranlarını artırabilir, maliyetleri düşürebilir ve klinik sonuçları iyileştirebilir. YÜT alanında YZ' nin entegrasyonunun, gelecekte daha verimli ve etkili tedavi süreçlerinin geliştirilmesine katkı sağlayacağı öngörülmektedir.

Anahtar Kelimeler

• Yapay zeka
• Yardımcı Üreme Teknikleri
• Embriyoloji.

Kaynakça

1. Smajdor A, Villalba A. The Ethics of Cellular Reprogramming. Cell Reprogram. 2023;25(5):190-194.
2. Haenlein, M., Kaplan, A.: A brief history of artificial intelligence: on the past, present, and future of artificial intelligence. Calif.Manag. Rev. 61, 5–14 (2019).
3. Wang R, Pan W, Jin L, et al. Artificial Intelligence in reproductive medicine. Reproduction. 2019;158(4): R139-R154.
4. Zaninovic N, Rosenwaks Z. Artificial intelligence in human in vitro fertilization and embryology. Fertil Steril.2020;114(5):914-920.
5. Curchoe CL, Bormann CL. Artificial intelligence and machine learning for human reproduction and embryology presented at ASRM and ESHRE 2018. J Assist Reprod Genet. 2019;36(4):591-600.
6. Medenica S, Zivanovic D, Batkoska L, Marinelli S, Basile G, Perino A, Cucinella G, Gullo G, Zaami S. The Future Is Coming: Artificial Intelligence in the Treatment of Infertility Could Improve Assisted Reproduction Outcomes-The Value of Regulatory Frameworks. Diagnostics (Basel). 2022 Nov 28;12(12):2979.
7. Ma, Y.; Wang, Z.; Yang, H.; Yang, L. Artificial intelligence applications in the development of autonomous vehicles: A survey. IEEE/CAA J. Autom. Sin. 2020, 7, 315–329.
8. Negnevitsky, Michael. Artificial Intelligence: A Guide to Intelligent Systems. 2nd ed., Addison-Wesley, 2005.

9. Gao Y, Chen Y, Jiang Y, et al. Artificial Intelligence Algorithm-Based Feature Extraction of Computed Tomography Images and Analysis of Benign and Malignant Pulmonary Nodules. Comput Intell Neurosci. 2022; 2022:5762623. Published 2022 Sep 14.
10. Durkee MS, Abraham R, Clark MR, Giger ML. Artificial Intelligence and Cellular Segmentation in Tissue Microscopy Images. Am J Pathol. 2021;191(10):1693-1701.
11. Diakiw SM, Hall JMM, VerMilyea MD, et al. Development of an artificial intelligence model for predicting the likelihood of human embryo euploidy based on blastocyst images from multiple imaging systems during IVF. Hum Reprod. 2022;37(8):1746-1759.
12. D'Antoni F, Russo F, Ambrosio L, et al. Artificial Intelligence and Computer Vision in Low Back Pain: A Systematic Review. Int J Environ Res Public Health. 2021;18(20):10909. Published 2021 Oct 17.
13. VerMilyea M, Hall JMM, Diakiw SM, et al. Development of an artificial intelligence-based assessment model for prediction of embryo viability using static images captured by optical light microscopy during IVF. Hum Reprod. 2020;35(4):770-784.
14. Ramesh AN, Kambhampati C, Monson JR, Drew PJ. Artificial intelligence in medicine. Ann R Coll Surg Engl. 2004;86(5):334-338.
15. Gupta R, Srivastava D, Sahu M, Tiwari S, Ambasta RK, Kumar P. Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Mol Divers. 2021;25(3):1315-1360.
16. Jiang VS, Pavlovic ZJ, Hariton E. The Role of Artificial Intelligence and Machine Learning in Assisted Reproductive Technologies. Obstet Gynecol Clin North Am. 2023;50(4):747-762.
17. Zhao M, Xu M, Li H, et al. Application of convolutional neural network on early human embryo segmentation during in vitro fertilization. J Cell Mol Med. 2021;25(5):2633-2644.
18. Illingworth PJ, Venetis C, Gardner DK, et al. Deep learning versus manual morphology-based embryo selection in IVF: a randomized, double-blind noninferiority trial. Nat Med. 2024;30(11):3114-3120.
19. Abduljabbar, R.; Dia, H.; Liyanage, S.; Bagloee, S.A. Applications of Artificial Intelligence in Transport: An Overview. Sustainability 2019, 11, 189.
20. Dimitriadis I, Zaninovic N, Badiola AC, Bormann CL. Artificial intelligence in the embryology laboratory: a review. Reprod Biomed Online. 2022;44(3):435-448.
21. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015 May 28;521(7553):436-44.
22. Mintz Y, Brodie R. Introduction to artificial intelligence in medicine. Minim Invasive Ther Allied Technol [Internet]. 2019;28(2):73–81
23. Larentzakis A, Lygeros N. Artificial intelligence (AI) in medicine as a strategic valuable tool. Pan Afr Med J. 2021 Feb 17; 38:184. doi: 10.11604/pamj.2021.38.184.28197. PMID: 33995790; PMCID: PMC8106796.
24. Cote MP, Lubowitz JH, Brand JC, Rossi MJ. Artificial Intelligence, Machine Learning, and Medicine: A Little Background Goes a Long Way Toward Understanding. Arthroscopy. 2021 Jun;37(6):1699-1702.
25. Mortimer, S. T., van der Horst, G., and Mortimer, D. (2015). The future of computer-aided sperm analysis. Asian J. Androl. 17, 545–553.
26. Amann, R. P., and Katz, D. F. (2004). Andrology lab corner: reflections on CASA after 25 years. J. Androl. 25, 317–325.
27. Yeste M, Bonet S, Rodríguez-Gil JE, Rivera Del Álamo MM. Evaluation of sperm motility with CASA-Mot: which factors may influence our measurements? Reprod Fertil Dev. 2018;30(6):789-798.
28. Sivanarayana T, Krishna ChR, Prakash GJ, Krishna KM, Madan K, Rani BS, Sudhakar G, Raju GA. CASA derived human sperm abnormalities: correlation with chromatin packing and DNA fragmentation. J Assist Reprod Genet. 2012 Dec;29(12):1327-34.
29. Girela JL, Gil D, Johnsson M, Gomez-Torres MJ, De Juan J. Semen parameters can be predicted from environmental factors and lifestyle using artificial intelligence methods. Biol Reprod. 2013;88(4):99. Published 2013 Apr 18.
30. Sahoo AJ, Kumar Y. Seminal quality prediction using data mining methods. Technol Health Care. 2014;22(4):531-545. doi:10.3233/THC-140816
31. Si K, Huang B, Jin L. Application of artificial intelligence in gametes and embryos selection. Hum Fertil (Camb). 2023;26(4):757-777.
32. Cavalera F, Zanoni M, Merico V, et al. A Neural Network-Based Identification of Developmentally Competent or Incompetent Mouse Fully-Grown Oocytes. J Vis Exp. 2018;(133):56668. Published 2018 Mar 3.
33. Kragh MF, Karstoft H. Embryo selection with artificial intelligence: how to evaluate and compare methods? J Assist Reprod Genet. 2021;38(7):1675-1689.
34. Lemmen JG, Agerholm I, Ziebe S. Kinetic markers of human embryo quality using time-lapse recordings of IVF/ICSI-fertilized oocytes. Reprod Biomed Online. 2008; 17:385–91.
35. Andersen AN, Goossens V, Ferraretti AP, et al. Assisted reproductive technology in Europe, 2004: results generated from European registers by ESHRE. Hum Reprod. 2008; 23:756–71.
36. Luong TM, Le NQK. Artificial intelligence in time-lapse system: advances, applications, and future perspectives in reproductive medicine. J Assist Reprod Genet. 2024;41(2):239-252.
37. Kaser DJ, Racowsky C. Clinical outcomes following selection of human preimplantation embryos with time-lapse monitoring: a systematic review. Hum Reprod Update. 2014; 20:617–31
38. Dal Canto M, Coticchio G, Mignini Renzini M, et al. Cleavage kinetics analysis of human embryos predicts development to blastocyst and implantation. Reprod Biomed Online. 2012; 25:474–80.
39. Yao-Yu Z, Yan X, Rui-Huan G, et al. Correlation between embryo morphokinetic parameters and euploidy [in Chinese]. J Reprod Med. 2020; 29:1275–9.
40. Reignier A, Lammers J, Barriere P, Freour T. Can time-lapse parameters predict embryo ploidy? A systematic review. Reprod Biomed Online. 2018 Apr;36(4):380-387.
41. Wang J, Guo Y, Zhang N, Li T. Research progress of time-lapse imaging technology and embryonic development potential: A review. Medicine (Baltimore). 2023;102(38): e35203.
42. Santos Filho E, Noble JA, Poli M, Griffiths T, Emerson G, Wells D. A method for semi-automatic grading of human blastocyst microscope images. Hum Reprod. 2012;27(9):2641-2648.
43. Anagnostopoulou C, Maldonado Rosas I, Gugnani N, et al. An expert commentary on essential equipment, supplies and culture media in the assisted reproductive technology laboratory. Panminerva Med. 2022;64(2):140-155.
44. Dominguez A, Garrido N, Pellicer A, Meseguer M. New methods to assess embryo viability: state of the art. Curr Opin Obstet Gynecol. 2011;23(4):245-251.
45. Rosenwaks Z, Zaninovic N. Artificial intelligence in assisted reproduction: Embryo assessment and beyond. Fertil Steril. 2021;116(5):1246-1251.
46. Li Y, Wang F, Yang S, et al. A deep learning approach to predict embryo viability based on time-lapse imaging: a multi-center study. Hum Reprod. 2022;37(4):885-895.