A review of artificial intelligence applications in in vitro fertilization

Shah PK, Gher JM. Human rights approaches to reducing infertility. Int J Gynaecol Obstet. 2023;162(1):368–74.

Article  PubMed  Google Scholar 

De Geyter C, Wyns C, Calhaz-Jorge C, de Mouzon J, Ferraretti AP, Kupka M, et al. 20 years of the European IVF-monitoring Consortium registry: what have we learned? A comparison with registries from two other regions. Hum Reprod. 2020;35(12):2832–49.

Article  PubMed Central  PubMed  Google Scholar 

Villani MT, Morini D, Spaggiari G, Falbo AI, Melli B, La Sala GB, et al. Are sperm parameters able to predict the success of assisted reproductive technology? A retrospective analysis of over 22,000 assisted reproductive technology cycles. Andrology. 2022;10(2):310–21.

Article  PubMed  Google Scholar 

Chu KY, Nassau DE, Arora H, Lokeshwar SD, Madhusoodanan V, Ramasamy R. Artificial intelligence in reproductive urology. Curr Urol Rep. 2019;20(9):52.

Article  PubMed  Google Scholar 

Louis CM, Erwin A, Handayani N, Polim AA, Boediono A, Sini I. Review of computer vision application in in vitro fertilization: the application of deep learning-based computer vision technology in the world of IVF. J Assist Reprod Genet. 2021;38(7):1627–39.

Article  PubMed Central  PubMed  Google Scholar 

Khosravi P, Kazemi E, Zhan Q, Malmsten JE, Toschi M, Zisimopoulos P, et al. Deep learning enables robust assessment and selection of human blastocysts after in vitro fertilization. NPJ Digit Med. 2019;2:21.

Article  PubMed Central  PubMed  Google Scholar 

Bhaskar D, Chang TA, Wang S. Current trends in artificial intelligence in reproductive endocrinology. Curr Opin Obstet Gynecol. 2022;34(4):159–63.

Article  PubMed  Google Scholar 

Dong X, Chen G, Zhu Y, Ma B, Ban X, Wu N, et al. Artificial intelligence in skeletal metastasis imaging. Comput Struct Biotechnol J. 2024;23:157–64.

Article  PubMed  Google Scholar 

Rosenwaks Z. Artificial intelligence in reproductive medicine: a fleeting concept or the wave of the future? Fertil Steril. 2020;114(5):905–7.

Article  CAS  PubMed  Google Scholar 

Hanassab S, Abbara A, Yeung AC, Voliotis M, Tsaneva-Atanasova K, Kelsey TW, et al. The prospect of artificial intelligence to personalize assisted reproductive technology. NPJ Digit Med. 2024;7(1):55.

Article  PubMed Central  PubMed  Google Scholar 

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.

Article  PubMed Central  PubMed  Google Scholar 

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):747–62.

Article  PubMed  Google Scholar 

Bormann CL, Kanakasabapathy MK, Thirumalaraju P, Gupta R, Pooniwala R, Kandula H, et al. Performance of a deep learning based neural network in the selection of human blastocysts for implantation. Elife. 2020;9. https://doi.org/10.7554/eLife.55301

Curchoe CL, Bormann C, Hammond E, Salter S, Timlin C, Williams LB, et al. Assuring quality in assisted reproduction laboratories: assessing the performance of ART Compass - a digital art staff management platform. J Assist Reprod Genet. 2023;40(2):265–78.

Article  PubMed Central  PubMed  Google Scholar 

Jiang VS, Bormann CL. Artificial intelligence in the in vitro fertilization laboratory: a review of advancements over the last decade. Fertil Steril. 2023;120(1):17–23.

Article  PubMed  Google Scholar 

Coelho Neto MA, Ludwin A, Borrell A, Benacerraf B, Dewailly D, da Silva CF, et al. Counting ovarian antral follicles by ultrasound: a practical guide. Ultrasound Obstet Gynecol. 2018;51(1):10–20.

Article  CAS  PubMed  Google Scholar 

Li H, Fang J, Liu S, Liang X, Yang X, Mai Z, et al. CR-Unet: a composite network for ovary and follicle segmentation in ultrasound images. IEEE J Biomed Health Inform. 2020;24(4):974–83.

Article  PubMed  Google Scholar 

Mathur P, Kakwani K, Diplav KS, Ga R. Deep learning based quantification of ovary and follicles using 3D transvaginal ultrasound in assisted reproduction. Annu Int Conf IEEE Eng Med Biol Soc. 2020;2020:2109–12.

PubMed  Google Scholar 

Yang X, Li H, Wang Y, Liang X, Chen C, Zhou X, et al. Contrastive rendering with semi-supervised learning for ovary and follicle segmentation from 3D ultrasound. Med Image Anal. 2021;73:102134.

Article  PubMed  Google Scholar 

Liang X, Liang J, Zeng F, Lin Y, Li Y, Cai K, et al. Evaluation of oocyte maturity using artificial intelligence quantification of follicle volume biomarker by three-dimensional ultrasound. Reprod Biomed Online. 2022;45(6):1197–206.

Article  CAS  PubMed  Google Scholar 

Noor N, Vignarajan CP, Malhotra N, Vanamail P. Three-dimensional automated volume calculation (sonography-based automated volume count) versus two-dimensional manual ultrasonography for follicular tracking and oocyte retrieval in women undergoing in vitro fertilization-embryo transfer: a randomized controlled trial. J Hum Reprod Sci. 2020;13(4):296–302.

Article  PubMed Central  PubMed  Google Scholar 

Manna C, Nanni L, Lumini A, Pappalardo S. Artificial intelligence techniques for embryo and oocyte classification. Reprod Biomed Online. 2013;26(1):42–9.

Article  PubMed  Google Scholar 

Targosz A, Przystalka P, Wiaderkiewicz R, Mrugacz G. Semantic segmentation of human oocyte images using deep neural networks. Biomed Eng Online. 2021;20(1):40.

Article  PubMed Central  PubMed  Google Scholar 

Fjeldstad J, Qi W, Mercuri N, Siddique N, Meriano J, Krivoi A, et al. An artificial intelligence tool predicts blastocyst development from static images of fresh mature oocytes. Reprod Biomed Online. 2024;48(6):103842.

Article  PubMed  Google Scholar 

Boylan CF, Sambo KM, Neal-Perry G, Brayboy LM. Ex ovo omnia-why don’t we know more about egg quality via imaging? Biol Reprod. 2024;110(6):1201–12.

Article  PubMed Central  PubMed  Google Scholar 

Ferrand T, Boulant J, He C, Chambost J, Jacques C, Pena CA, et al. Predicting the number of oocytes retrieved from controlled ovarian hyperstimulation with machine learning. Hum Reprod. 2023;38(10):1918–26.

Article  PubMed Central  PubMed  Google Scholar 

Simopoulou M, Sfakianoudis K, Maziotis E, Antoniou N, Rapani A, Anifandis G, et al. Are computational applications the “crystal ball” in the IVF laboratory? The evolution from mathematics to artificial intelligence. J Assist Reprod Genet. 2018;35(9):1545–57.

Article  PubMed Central  PubMed  Google Scholar 

Basile N, Elkhatib I, Meseguer M. A strength, weaknesses, opportunities and threats analysis on time lapse. Curr Opin Obstet Gynecol. 2019;31(3):148–55.

Article  PubMed  Google Scholar 

Zaninovic N, Rosenwaks Z. Artificial intelligence in human in vitro fertilization and embryology. Fertil Steril. 2020;114(5):914–20.

Article  CAS  PubMed  Google Scholar 

Capalbo A, Rienzi L, Cimadomo D, Maggiulli R, Elliott T, Wright G, et al. Correlation between standard blastocyst morphology, euploidy and implantation: an observational study in two centers involving 956 screened blastocysts. Hum Reprod. 2014;29(6):1173–81.

Article  PubMed  Google Scholar 

Gardner DK, Meseguer M, Rubio C, Treff NR. Diagnosis of human preimplantation embryo viability. Hum Reprod Update. 2015;21(6):727–47.

Article  CAS  PubMed  Google Scholar 

Rubio I, Galan A, Larreategui Z, Ayerdi F, Bellver J, Herrero J, et al. Clinical validation of embryo culture and selection by morphokinetic analysis: a randomized, controlled trial of the EmbryoScope. Fertil Steril. 2014;102(5):1287-94 e5.

Article  PubMed  Google Scholar 

Meng Q, Xu Y, Zheng A, Li H, Ding J, Xu Y, et al. Noninvasive embryo evaluation and selection by time-lapse monitoring vs. conventional morphologic assessment in women undergoing in vitro fertilization/intracytoplasmic sperm injection: a single-center randomized controlled study. Fertil Steril. 2022;117(6):1203–12.

Article  PubMed 

留言 (0)

沒有登入
gif