ART’de Yapay Zekanın Yeri

Fauser BC. Towards the global coverage of a unified registry of IVF outcomes. Reprod Biomed Online. 2019 Feb;38(2):133–7.

Carson SA, Kallen AN. Diagnosis and Management of Infertility: A Review. JAMA. 2021 Jul 6;326(1):65–76.

Andreu-Perez J, Poon CCY, Merrifield RD, Wong STC, Yang GZ. Big data for health. IEEE J Biomed Health Inform. 2015 Jul;19(4):1193–208.

Yu Z, Li M, Peng W. Exploring biomarkers of premature ovarian insufficiency based on oxford nanopore transcriptional profile and machine learning. Sci Rep. 2023 Jul 17;13(1):11498.

Qu Y, Chen M, Wang Y, Qu L, Wang R, Liu H, et al. Rapid screening of infertility-associated gynecological conditions via ambient glow discharge mass spectrometry utilizing urine metabolic fingerprints. Talanta. 2024 Jul 1;274:125969.

Jakubczyk P, Paja W, Pancerz K, Cebulski J, Depciuch J, Uzun Ö, et al. Determination of idiopathic female infertility from infrared spectra of follicle fluid combined with gonadotrophin levels, multivariate analysis and machine learning methods. Photodiagnosis Photodyn Ther. 2022 Jun;38:102883.

Sengupta P, Dutta S, Liew F, Samrot A, Dasgupta S, Rajput MA, et al. Reproductomics: Exploring the Applications and Advancements of Computational Tools. Physiol Res. 2024 Nov 12;73(5):687–702.

Dabi Y, Suisse S, Marie Y, Delbos L, Poilblanc M, Descamps P, et al. New class of RNA biomarker for endometriosis diagnosis: The potential of salivary piRNA expression. Eur J Obstet Gynecol Reprod Biol. 2023 Dec;291:88–95.

Chen P, Yang M, Wang Y, Guo Y, Liu Y, Fang C, et al. Aging endometrium in young women: molecular classification of endometrial aging-based markers in women younger than 35 years with recurrent implantation failure. J Assist Reprod Genet. 2022 Sep;39(9):2143–51.

Wang R, Pan W, Jin L, Li Y, Geng Y, Gao C, et al. Artificial intelligence in reproductive medicine. Reproduction. 2019 Oct;158(4):R139–54.

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

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 Mar 1;7(1):55.

Jiang VS, Pavlovic ZJ, Hariton E. The Role of Artificial Intelligence and Machine Learning in Assisted Reproductive Technologies. Obstet Gynecol Clin North Am. 2023 Dec;50(4):747–62.

Cohen J, Silvestri G, Paredes O, Martin-Alcala HE, Chavez-Badiola A, Alikani M, et al. Artificial intelligence in assisted reproductive technology: separating the dream from reality. Reprod Biomed Online. 2025 Apr;50(4):104855.

Jenkins J, van der Poel S, Krüssel J, Bosch E, Nelson SM, Pinborg A, et al. Empathetic application of machine learning may address appropriate utilization of ART. Reprod Biomed Online. 2020 Oct;41(4):573–7.

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

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 Apr;24(4):974–83.

Mathur P, Kakwani K, Diplav null, Kudavelly S, 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 Jul;2020:2109–12.

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 Oct;73:102134.

Noor N, Vignarajan C, 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.

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 Dec;45(6):1197–206.

Abbara A, Patel A, Hunjan T, Clarke SA, Chia G, Eng PC, et al. FSH Requirements for Follicle Growth During Controlled Ovarian Stimulation. Front Endocrinol (Lausanne). 2019;10:579.

Broekmans FJ. Individualization of FSH Doses in Assisted Reproduction: Facts and Fiction. Front Endocrinol (Lausanne). 2019;10:181.

Fanton M, Nutting V, Rothman A, Maeder-York P, Hariton E, Barash O, et al. An interpretable machine learning model for individualized gonadotrophin starting dose selection during ovarian stimulation. Reprod Biomed Online. 2022 Dec;45(6):1152–9.

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 Oct 3;38(10):1918–26.

Ishihara O, Arce JC, Japanese Follitropin Delta Phase 3 Trial (STORK) Group. Individualized follitropin delta dosing reduces OHSS risk in Japanese IVF/ICSI patients: a randomized controlled trial. Reprod Biomed Online. 2021 May;42(5):909–18.

Correa N, Cerquides J, Arcos JL, Vassena R, Popovic M. Personalizing the first dose of FSH for IVF/ICSI patients through machine learning: a non-inferiority study protocol for a multi-center randomized controlled trial. Trials. 2024 Jan 11;25(1):38.

Correa N, Cerquides J, Arcos JL, Vassena R. Supporting first FSH dosage for ovarian stimulation with machine learning. Reprod Biomed Online. 2022 Nov;45(5):1039–45.

Zieliński K, Pukszta S, Mickiewicz M, Kotlarz M, Wygocki P, Zieleń M, et al. Personalized prediction of the secondary oocytes number after ovarian stimulation: A machine learning model based on clinical and genetic data. PLoS Comput Biol. 2023 Apr;19(4):e1011020.

Hariton E, Chi EA, Chi G, Morris JR, Braatz J, Rajpurkar P, et al. A machine learning algorithm can optimize the day of trigger to improve in vitro fertilization outcomes. Fertil Steril. 2021 Nov;116(5):1227–35.

AlSaad R, Abd-Alrazaq A, Choucair F, Ahmed A, Aziz S, Sheikh J. Harnessing Artificial Intelligence to Predict Ovarian Stimulation Outcomes in In Vitro Fertilization: Scoping Review. J Med Internet Res. 2024 Jul 5;26:e53396.

Fanton M, Nutting V, Solano F, Maeder-York P, Hariton E, Barash O, et al. An interpretable machine learning model for predicting the optimal day of trigger during ovarian stimulation. Fertil Steril. 2022 Jul;118(1):101–8.

Letterie G, Mac Donald A. Artificial intelligence in in vitro fertilization: a computer decision support system for day-to-day management of ovarian stimulation during in vitro fertilization. Fertil Steril. 2020 Nov;114(5):1026–31.

Robertson I, Chmiel FP, Cheong Y. Streamlining follicular monitoring during controlled ovarian stimulation: a data-driven approach to efficient IVF care in the new era of social distancing. Hum Reprod. 2021 Jan 1;36(1):99–106.

Letterie G, MacDonald A, Shi Z. An artificial intelligence platform to optimize workflow during ovarian stimulation and IVF: process improvement and outcome-based predictions. Reprod Biomed Online. 2022 Feb;44(2):254–60.

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

Fjeldstad J, Qi W, Siddique N, Mercuri N, Nayot D, Krivoi A. Segmentation of mature human oocytes provides interpretable and improved blastocyst outcome predictions by a machine learning model. Sci Rep. 2024 May 8;14(1):10569.

Borges E, Braga D, Del Collado M, Iaconelli A, Fjeldstad J, Mercuri N, et al. Artificial intelligence-driven oocyte assessment for predicting blastulation and high-quality blastocyst formation in severe male factor infertility. F S Sci. 2025 Jul 15;S2666-335X(25)00047-3.

Finelli R, Leisegang K, Tumallapalli S, Henkel R, Agarwal A. The validity and reliability of computer-aided semen analyzers in performing semen analysis: a systematic review. Transl Androl Urol. 2021 Jul;10(7):3069–79.

Hicks SA, Andersen JM, Witczak O, Thambawita V, Halvorsen P, Hammer HL, et al. Machine Learning-Based Analysis of Sperm Videos and Participant Data for Male Fertility Prediction. Sci Rep. 2019 Nov 14;9(1):16770.

Ottl S, Amiriparian S, Gerczuk M, Schuller BW. motilitAI: A machine learning framework for automatic prediction of human sperm motility. iScience. 2022 Aug 19;25(8):104644.

Mendizabal-Ruiz G, Chavez-Badiola A, Aguilar Figueroa I, Martinez Nuño V, Flores-Saiffe Farias A, Valencia-Murilloa R, et al. Computer software (SiD) assisted real-time single sperm selection associated with fertilization and blastocyst formation. Reprod Biomed Online. 2022 Oct;45(4):703–11.

You JB, McCallum C, Wang Y, Riordon J, Nosrati R, Sinton D. Machine learning for sperm selection. Nat Rev Urol. 2021 Jul;18(7):387–403.

Montjean D, Godin Pagé MH, Pacios C, Calvé A, Hamiche G, Benkhalifa M, et al. Automated Single-Sperm Selection Software (SiD) during ICSI: A Prospective Sibling Oocyte Evaluation. Med Sci (Basel). 2024 Mar 27;12(2):19.

Movahed RA, Mohammadi E, Orooji M. Automatic segmentation of Sperm’s parts in microscopic images of human semen smears using concatenated learning approaches. Comput Biol Med. 2019 Jun;109:242–53.

Javadi S, Mirroshandel SA. A novel deep learning method for automatic assessment of human sperm images. Comput Biol Med. 2019 Jun;109:182–94.

Nashed JY, Liblik K, Dergham A, Witherspoon L, Flannigan R. Artificial Intelligence in Andrology: A New Frontier in Male Infertility Diagnosis and Treatment. Curr Urol Rep. 2025 Feb 24;26(1):29.

Wu DJ, Badamjav O, Reddy VV, Eisenberg M, Behr B. A preliminary study of sperm identification in microdissection testicular sperm extraction samples with deep convolutional neural networks. Asian J Androl. 2021;23(2):135–9.

McCallum C, Riordon J, Wang Y, Kong T, You JB, Sanner S, et al. Deep learning-based selection of human sperm with high DNA integrity. Commun Biol. 2019;2:250.

Kuroda S, Karna KK, Raneen Sawaid Kaiyal, Sajal Gupta, Rakesh Sharma, Agarwal A. DEVELOPMENT OF A NOVEL ROBUST ARTIFICIAL INTELLIGENCE DEVELOPED SPERM DNA FRAGMENTATION TEST – PRELIMINARY FINDINGS. Fertility and Sterility. 2022 Oct;118(4):e307.

Esteves SC, Zini A, Coward RM, Evenson DP, Gosálvez J, Lewis SEM, et al. Sperm DNA fragmentation testing: Summary evidence and clinical practice recommendations. Andrologia. 2021 Mar;53(2):e13874.

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

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

Rubio I, Galán 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. Fertility and Sterility. 2014 Nov;102(5):1287-1294.e5.

Fruchter-Goldmeier Y, Kantor B, Ben-Meir A, Wainstock T, Erlich I, Levitas E, et al. An artificial intelligence algorithm for automated blastocyst morphometric parameters demonstrates a positive association with implantation potential. Sci Rep. 2023 Sep 5;13(1):14617.

Bori L, Paya E, Alegre L, Viloria TA, Remohi JA, Naranjo V, et al. Novel and conventional embryo parameters as input data for artificial neural networks: an artificial intelligence model applied for prediction of the implantation potential. Fertility and Sterility. 2020 Dec;114(6):1232–41.

Fordham DE, Rosentraub D, Polsky AL, Aviram T, Wolf Y, Perl O, et al. Embryologist agreement when assessing blastocyst implantation probability: is data-driven prediction the solution to embryo assessment subjectivity? Human Reproduction. 2022 Sep 30;37(10):2275–90.

Luong TMT, Le NQK. Artificial intelligence in time-lapse system: advances, applications, and future perspectives in reproductive medicine. J Assist Reprod Genet. 2024 Feb;41(2):239–52.

Reignier A, Girard JM, Lammers J, Chtourou S, Lefebvre T, Barriere P, et al. Performance of Day 5 KIDScoreTM morphokinetic prediction models of implantation and live birth after single blastocyst transfer. J Assist Reprod Genet. 2019 Nov;36(11):2279–85.

Tartia AP, Wu CQ, Gale J, Shmorgun D, Léveillé MC. Time-lapse KIDScoreD5 for prediction of embryo pregnancy potential in fresh and vitrified-warmed single-embryo transfers. Reproductive BioMedicine Online. 2022 Jul;45(1):46–53.

Ueno S, Berntsen J, Ito M, Okimura T, Kato K. Correlation between an annotation-free embryo scoring system based on deep learning and live birth/neonatal outcomes after single vitrified-warmed blastocyst transfer: a single-centre, large-cohort retrospective study. J Assist Reprod Genet. 2022 Sep;39(9):2089–99.

Ezoe K, Shimazaki K, Miki T, Takahashi T, Tanimura Y, Amagai A, et al. Association between a deep learning-based scoring system with morphokinetics and morphological alterations in human embryos. Reproductive BioMedicine Online. 2022 Dec;45(6):1124–32.

Johansen MN, Parner ET, Kragh MF, Kato K, Ueno S, Palm S, et al. Comparing performance between clinics of an embryo evaluation algorithm based on time-lapse images and machine learning. J Assist Reprod Genet. 2023 Sep;40(9):2129–37.

Ten J, Tio MC, Pini P, Kelley K, Guerrrero J, Rodríguez-Arnedo A, et al. Decoding embryo development: the effect of clinical variables in morphokinetics and artificial intelligence quality scoring. Reprod Biomed Online. 2025 Jul;51(1):104866.

Fitz VW, Kanakasabapathy MK, Thirumalaraju P, Kandula H, Ramirez LB, Boehnlein L, et al. Should there be an “AI” in TEAM? Embryologists selection of high implantation potential embryos improves with the aid of an artificial intelligence algorithm. J Assist Reprod Genet. 2021 Oct;38(10):2663–70.

Buldo-Licciardi J, Large MJ, McCulloh DH, McCaffrey C, Grifo JA. Utilization of standardized preimplantation genetic testing for aneuploidy (PGT-A) via artificial intelligence (AI) technology is correlated with improved pregnancy outcomes in single thawed euploid embryo transfer (STEET) cycles. J Assist Reprod Genet. 2023 Feb;40(2):289–99.

Chavez-Badiola A, Flores-Saiffe-Farías A, Mendizabal-Ruiz G, Drakeley AJ, Cohen J. Embryo Ranking Intelligent Classification Algorithm (ERICA): artificial intelligence clinical assistant predicting embryo ploidy and implantation. Reproductive BioMedicine Online. 2020 Oct;41(4):585–93.

VerMilyea M, Hall JMM, Diakiw SM, Johnston A, Nguyen T, Perugini D, 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 Apr 28;35(4):770–84.

Rajendran S, Brendel M, Barnes J, Zhan Q, Malmsten JE, Zisimopoulos P, et al. Automatic ploidy prediction and quality assessment of human blastocysts using time-lapse imaging. Nat Commun. 2024 Sep 5;15(1):7756.

Li X, Yao Y, Zhao D, Chang X, Li Y, Lin H, et al. Clinical outcomes of single blastocyst transfer with machine learning guided noninvasive chromosome screening grading system in infertile patients. Reprod Biol Endocrinol. 2024 May 23;22(1):61.

Diakiw SM, Hall JMM, VerMilyea M, Lim AYX, Quangkananurug W, Chanchamroen S, et al. An artificial intelligence model correlated with morphological and genetic features of blastocyst quality improves ranking of viable embryos. Reproductive BioMedicine Online. 2022 Dec;45(6):1105–17.

Popovic M, Borot L, Lorenzon AR, Lopes ALR de C, Sakkas D, Lledó B, et al. Implicit bias in diagnosing mosaicism amongst preimplantation genetic testing providers: results from a multicenter study of 36 395 blastocysts. Hum Reprod. 2024 Jan 5;39(1):258–74.

Muñoz E, Bronet F, Lledo B, Palacios-Verdú G, Martinez-Rocca L, Altmäe S, et al. To transfer or not to transfer: the dilemma of mosaic embryos - a narrative review. Reprod Biomed Online. 2024 Mar;48(3):103664.

Greco E, Minasi MG, Fiorentino F. Healthy Babies after Intrauterine Transfer of Mosaic Aneuploid Blastocysts. N Engl J Med. 2015 Nov 19;373(21):2089–90.

Ortiz JA, Morales R, Lledó B, Vicente JA, González J, García-Hernández EM, et al. Application of machine learning to predict aneuploidy and mosaicism in embryos from in vitro fertilization cycles. AJOG Glob Rep. 2022 Nov;2(4):100103.

Ma BX, Zhao GN, Yi ZF, Yang YL, Jin L, Huang B. Enhancing clinical utility: deep learning-based embryo scoring model for non-invasive aneuploidy prediction. Reprod Biol Endocrinol. 2024 May 22;22(1):58.

Zou Y, Pan Y, Ge N, Xu Y, Gu R, Li Z, et al. Can the combination of time-lapse parameters and clinical features predict embryonic ploidy status or implantation? Reprod Biomed Online. 2022 Oct;45(4):643–51.

Wald M, Sparks AET, Sandlow J, Van-Voorhis B, Syrop CH, Niederberger CS. Computational models for prediction of IVF/ICSI outcomes with surgically retrieved spermatozoa. Reprod Biomed Online. 2005 Sep;11(3):325–31.

Benchaib M, Labrune E, Giscard d’Estaing S, Salle B, Lornage J. Shallow artificial networks with morphokinetic time-lapse parameters coupled to ART data allow to predict live birth. Reprod Med Biol. 2022;21(1):e12486.

Kato K, Ueno S, Berntsen J, Ito M, Shimazaki K, Uchiyama K, et al. Comparing prediction of ongoing pregnancy and live birth outcomes in patients with advanced and younger maternal age patients using KIDScoreTM day 5: a large-cohort retrospective study with single vitrified-warmed blastocyst transfer. Reprod Biol Endocrinol. 2021 Jul 2;19(1):98.

Curchoe CL. Proceedings of the first world conference on AI in fertility. J Assist Reprod Genet. 2023 Feb;40(2):215–22.

Lee T, Natalwala J, Chapple V, Liu Y. A brief history of artificial intelligence embryo selection: from black-box to glass-box. Hum Reprod. 2024 Feb 1;39(2):285–92.

Khan I, Khare BK. Exploring the potential of machine learning in gynecological care: a review. Arch Gynecol Obstet. 2024 Jun;309(6):2347–65.

Jiang VS, Bormann CL. Noninvasive genetic screening: current advances in artificial intelligence for embryo ploidy prediction. Fertil Steril. 2023 Aug;120(2):228–34.

Hall JMM, Nguyen TV, Dinsmore AW, Perugini D, Perugini M, Fukunaga N, et al. Use of federated learning to develop an artificial intelligence model predicting usable blastocyst formation from pre-ICSI oocyte images. Reprod Biomed Online. 2024 Dec;49(6):104403.

Liao Q, Zhang Q, Feng X, Huang H, Xu H, Tian B, et al. Development of deep learning algorithms for predicting blastocyst formation and quality by time-lapse monitoring. Commun Biol. 2021 Mar 26;4(1):415.

Jin L, Si K, Li Z, He H, Wu L, Ma B, et al. Multiple collapses of blastocysts after full blastocyst formation is an independent risk factor for aneuploidy - a study based on AI and manual validation. Reprod Biol Endocrinol. 2024 Jul 15;22(1):81.

Chen F, Xie X, Cai D, Yan P, Ding C, Wen Y, et al. Knowledge-embedded spatio-temporal analysis for euploidy embryos identification in couples with chromosomal rearrangements. Chin Med J (Engl). 2024 Mar 20;137(6):694–703.

Salih M, Austin C, Warty RR, Tiktin C, Rolnik DL, Momeni M, et al. Embryo selection through artificial intelligence versus embryologists: a systematic review. Hum Reprod Open. 2023;2023(3):hoad031.

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 Jun;48(6):103842.

Zou H, Wang R, Morbeck DE. Diagnostic or prognostic? Decoding the role of embryo selection on in vitro fertilization treatment outcomes. Fertil Steril. 2024 May;121(5):730–6.

Wang G, Wang K, Gao Y, Chen L, Gao T, Ma Y, et al. A generalized AI system for human embryo selection covering the entire IVF cycle via multi-modal contrastive learning. Patterns (N Y). 2024 Jul 12;5(7):100985.

Fauser BC. Towards the global coverage of a unified registry of IVF outcomes. Reprod Biomed Online. 2019 Feb;38(2):133–7.

Carson SA, Kallen AN. Diagnosis and Management of Infertility: A Review. JAMA. 2021 Jul 6;326(1):65–76.

Andreu-Perez J, Poon CCY, Merrifield RD, Wong STC, Yang GZ. Big data for health. IEEE J Biomed Health Inform. 2015 Jul;19(4):1193–208.

Yu Z, Li M, Peng W. Exploring biomarkers of premature ovarian insufficiency based on oxford nanopore transcriptional profile and machine learning. Sci Rep. 2023 Jul 17;13(1):11498.

Qu Y, Chen M, Wang Y, Qu L, Wang R, Liu H, et al. Rapid screening of infertility-associated gynecological conditions via ambient glow discharge mass spectrometry utilizing urine metabolic fingerprints. Talanta. 2024 Jul 1;274:125969.

Jakubczyk P, Paja W, Pancerz K, Cebulski J, Depciuch J, Uzun Ö, et al. Determination of idiopathic female infertility from infrared spectra of follicle fluid combined with gonadotrophin levels, multivariate analysis and machine learning methods. Photodiagnosis Photodyn Ther. 2022 Jun;38:102883.

Sengupta P, Dutta S, Liew F, Samrot A, Dasgupta S, Rajput MA, et al. Reproductomics: Exploring the Applications and Advancements of Computational Tools. Physiol Res. 2024 Nov 12;73(5):687–702.

Dabi Y, Suisse S, Marie Y, Delbos L, Poilblanc M, Descamps P, et al. New class of RNA biomarker for endometriosis diagnosis: The potential of salivary piRNA expression. Eur J Obstet Gynecol Reprod Biol. 2023 Dec;291:88–95.

Chen P, Yang M, Wang Y, Guo Y, Liu Y, Fang C, et al. Aging endometrium in young women: molecular classification of endometrial aging-based markers in women younger than 35 years with recurrent implantation failure. J Assist Reprod Genet. 2022 Sep;39(9):2143–51.

Wang R, Pan W, Jin L, Li Y, Geng Y, Gao C, et al. Artificial intelligence in reproductive medicine. Reproduction. 2019 Oct;158(4):R139–54.

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

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 Mar 1;7(1):55.

Jiang VS, Pavlovic ZJ, Hariton E. The Role of Artificial Intelligence and Machine Learning in Assisted Reproductive Technologies. Obstet Gynecol Clin North Am. 2023 Dec;50(4):747–62.

Cohen J, Silvestri G, Paredes O, Martin-Alcala HE, Chavez-Badiola A, Alikani M, et al. Artificial intelligence in assisted reproductive technology: separating the dream from reality. Reprod Biomed Online. 2025 Apr;50(4):104855.

Jenkins J, van der Poel S, Krüssel J, Bosch E, Nelson SM, Pinborg A, et al. Empathetic application of machine learning may address appropriate utilization of ART. Reprod Biomed Online. 2020 Oct;41(4):573–7.

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

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 Apr;24(4):974–83.

Mathur P, Kakwani K, Diplav null, Kudavelly S, 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 Jul;2020:2109–12.

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 Oct;73:102134.

Noor N, Vignarajan C, 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.

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 Dec;45(6):1197–206.

Abbara A, Patel A, Hunjan T, Clarke SA, Chia G, Eng PC, et al. FSH Requirements for Follicle Growth During Controlled Ovarian Stimulation. Front Endocrinol (Lausanne). 2019;10:579.

Broekmans FJ. Individualization of FSH Doses in Assisted Reproduction: Facts and Fiction. Front Endocrinol (Lausanne). 2019;10:181.

Fanton M, Nutting V, Rothman A, Maeder-York P, Hariton E, Barash O, et al. An interpretable machine learning model for individualized gonadotrophin starting dose selection during ovarian stimulation. Reprod Biomed Online. 2022 Dec;45(6):1152–9.

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 Oct 3;38(10):1918–26.

Ishihara O, Arce JC, Japanese Follitropin Delta Phase 3 Trial (STORK) Group. Individualized follitropin delta dosing reduces OHSS risk in Japanese IVF/ICSI patients: a randomized controlled trial. Reprod Biomed Online. 2021 May;42(5):909–18.

Correa N, Cerquides J, Arcos JL, Vassena R, Popovic M. Personalizing the first dose of FSH for IVF/ICSI patients through machine learning: a non-inferiority study protocol for a multi-center randomized controlled trial. Trials. 2024 Jan 11;25(1):38.

Correa N, Cerquides J, Arcos JL, Vassena R. Supporting first FSH dosage for ovarian stimulation with machine learning. Reprod Biomed Online. 2022 Nov;45(5):1039–45.

Zieliński K, Pukszta S, Mickiewicz M, Kotlarz M, Wygocki P, Zieleń M, et al. Personalized prediction of the secondary oocytes number after ovarian stimulation: A machine learning model based on clinical and genetic data. PLoS Comput Biol. 2023 Apr;19(4):e1011020.

Hariton E, Chi EA, Chi G, Morris JR, Braatz J, Rajpurkar P, et al. A machine learning algorithm can optimize the day of trigger to improve in vitro fertilization outcomes. Fertil Steril. 2021 Nov;116(5):1227–35.

AlSaad R, Abd-Alrazaq A, Choucair F, Ahmed A, Aziz S, Sheikh J. Harnessing Artificial Intelligence to Predict Ovarian Stimulation Outcomes in In Vitro Fertilization: Scoping Review. J Med Internet Res. 2024 Jul 5;26:e53396.

Fanton M, Nutting V, Solano F, Maeder-York P, Hariton E, Barash O, et al. An interpretable machine learning model for predicting the optimal day of trigger during ovarian stimulation. Fertil Steril. 2022 Jul;118(1):101–8.

Letterie G, Mac Donald A. Artificial intelligence in in vitro fertilization: a computer decision support system for day-to-day management of ovarian stimulation during in vitro fertilization. Fertil Steril. 2020 Nov;114(5):1026–31.

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Letterie G, MacDonald A, Shi Z. An artificial intelligence platform to optimize workflow during ovarian stimulation and IVF: process improvement and outcome-based predictions. Reprod Biomed Online. 2022 Feb;44(2):254–60.

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