Authors : Dr. Maheshwari. M; Dr. Venugopal Reddy. I

Volume/Issue : Volume 10 - 2025, Issue 5 - May

Google Scholar : https://tinyurl.com/57xn3wjx

DOI : https://doi.org/10.38124/ijisrt/25may1396

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : In vitro fertilization (IVF) has transformed the treatment of infertility, with embryo selection being a critical determinant of success. Traditionally, embryologists relied on morphological grading and subjective evaluation. However, recent developments in artificial intelligence (AI) are reshaping embryo selection, offering objective, accurate, and predictive tools to enhance clinical outcomes. This article reviews the evolution of AI-based embryo assessment, explores its integration with time-lapse imaging and omics data, and analyses its impact on implantation and live birth rates. Ethical concerns, regulatory issues, and future directions are also discussed, highlighting AI’s potential to revolutionise reproductive medicine.

References :

1. VerMilyea M, Hall J, Diakiw SM, Johnston A, Nguyen T. Development of an artificial intelligence-based assessment model for predicting embryo viability using static images. Hum Reprod. 2020;35(9):2114–2128.
2. Kragh MF, Toftager M, Jørgensen FS, et al. Use of AI in predicting blastocyst viability: a clinical retrospective study. Reprod Biomed Online. 2021;42(5):950–957.
3. Meseguer M, Herrero J, Tejera A, et al. The use of morphokinetics as a predictor of embryo implantation. Hum Reprod. 2011;26(10):2658–2671.
4. Khosravi P, Kazemi E, Zhan Q, et al. Deep learning enables robust assessment and selection of human blastocysts after in vitro fertilization. NPJ Digit Med. 2019;2:21.
5. Liu Y, Chen M, Deng K, et al. AI in IVF: A review of current status and future prospects. Fertil Steril. 2021;116(5):1125–1133.
6. Tran D, Cooke S, Illingworth PJ. Deep learning as a predictive tool for selecting viable embryos. Lancet Digit Health. 2019;1(3):e137–e147.
7. Friedenthal J, Smith A, Miller K, et al. Use of artificial intelligence embryo selection improves IVF success. Reprod Biomed Online. 2022;44(2):225–234.
8. Apter S, Bachelot A, El Hachem H, et al. Ethical concerns in AI-assisted reproduction. Bioethics. 2023;37(1):45–53.
9. ESHRE ART Lab SIG. Good practice recommendations for artificial intelligence in IVF. Hum Reprod Open. 2022;2022(2):hoac021.
10. Capalbo A, Rienzi L, Cimadomo D, et al. AI-based decision support systems in IVF: From hype to hope. Trends Biotechnol. 2020;38(12):1276–1282.
11. ICMR. National Guidelines for Assisted Reproductive Technology. Indian Council of Medical Research; 2022.
12. Presagen Life Whisperer. https://www.presagen.com/lifewhisperer
13. Vitrolife iDAScore. https://www.vitrolife.com
14. Fairtility CHLOE. https://www.fairtility.com
15. Yang Y, et al. Machine learning in reproductive medicine: A scoping review. Comput Biol Med. 2022;147:105740.