{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T16:00:45Z","timestamp":1791388845442,"version":"4.3.3"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,1,31]],"date-time":"2025-01-31T00:00:00Z","timestamp":1738281600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,1,31]],"date-time":"2025-01-31T00:00:00Z","timestamp":1738281600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100018694","name":"HORIZON EUROPE Marie Sklodowska-Curie Actions","doi-asserted-by":"publisher","award":["812660"],"award-info":[{"award-number":["812660"]}],"id":[{"id":"10.13039\/100018694","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Reprod Biol Endocrinol"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Artificial intelligence (AI) models analyzing embryo time-lapse images have been developed to predict the likelihood of pregnancy following in vitro fertilization (IVF). However, limited research exists on methods ensuring AI consistency and reliability in clinical settings during its development and validation process. We present a methodology for developing and validating an AI model across multiple datasets to demonstrate reliable performance in evaluating blastocyst-stage embryos.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      This multicenter analysis utilizes time-lapse images, pregnancy outcomes, and morphologic annotations from embryos collected at 10 IVF clinics across 9 countries between 2018 and 2022. The four-step methodology for developing and evaluating the AI model include: (I) curating annotated datasets that represent the intended clinical use case; (II) developing and optimizing the AI model; (III) evaluating the AI\u2019s performance by assessing its discriminative power and associations with pregnancy probability across variable data; and (IV) ensuring interpretability and explainability by correlating AI scores with relevant morphologic features of embryo quality. Three datasets were used: the training and validation dataset (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200916,935 embryos), the blind test dataset (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u20091,708 embryos; 3 clinics), and the independent dataset (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u20097,445 embryos; 7 clinics) derived from previously unseen clinic cohorts.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The AI was designed as a deep learning classifier ranking embryos by score according to their likelihood of clinical pregnancy. Higher AI score brackets were associated with increased fetal heartbeat (FH) likelihood across all evaluated datasets, showing a trend of increasing odds ratios (OR). The highest OR was observed in the top G4 bracket (test dataset G4 score\u2009\u2265\u20097.5: OR 3.84; independent dataset G4 score\u2009\u2265\u20097.5: OR 4.01), while the lowest was in the G1 bracket (test dataset G1 score\u2009&lt;\u20094.0: OR 0.40; independent dataset G1 score\u2009&lt;\u20094.0: OR 0.45). AI score brackets G2, G3, and G4 displayed OR values above 1.0 (\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&lt;\u20090.05), indicating linear associations with FH likelihood. Average AI scores were consistently higher for FH-positive than for FH-negative embryos within each age subgroup. Positive correlations were also observed between AI scores and key morphologic parameters used to predict embryo quality.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Strong AI performance across multiple datasets demonstrates the value of our four-step methodology in developing and validating the AI as a reliable adjunct to embryo evaluation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12958-025-01351-w","type":"journal-article","created":{"date-parts":[[2025,1,31]],"date-time":"2025-01-31T04:09:57Z","timestamp":1738296597000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Application of a methodological framework for the development and multicenter validation of reliable artificial intelligence in embryo evaluation"],"prefix":"10.1186","volume":"23","author":[{"given":"D.","family":"Gilboa","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akhil","family":"Garg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M.","family":"Shapiro","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M.","family":"Meseguer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Y.","family":"Amar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"N.","family":"Lustgarten","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"N.","family":"Desai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"T.","family":"Shavit","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"V.","family":"Silva","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"A.","family":"Papatheodorou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"A.","family":"Chatziparasidou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S.","family":"Angras","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J. H.","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"L.","family":"Thiel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"C. L.","family":"Curchoe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Y.","family":"Tauber","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"D. S.","family":"Seidman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,1,31]]},"reference":[{"key":"1351_CR1","doi-asserted-by":"publisher","first-page":"727","DOI":"10.1093\/humupd\/dmu064","volume":"21","author":"DK Gardner","year":"2015","unstructured":"Gardner DK, Meseguer M, Rubio C, Treff NR. Diagnosis of human preimplantation embryo viability. Hum Reprod Update. 2015;21:727\u201347.","journal-title":"Hum Reprod Update"},{"key":"1351_CR2","doi-asserted-by":"publisher","first-page":"2345","DOI":"10.1093\/humrep\/14.9.2345","volume":"14","author":"E Van Royen","year":"1999","unstructured":"Van Royen E, Mangelschots K, De Neubourg D, Valkenburg M, Van de Meerssche M, Ryckaert G, et al. Characterization of a top quality embryo, a step towards single-embryo transfer. Hum Reprod. 1999;14:2345\u20139.","journal-title":"Hum Reprod"},{"key":"1351_CR3","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1093\/humrep\/deu295","volume":"30","author":"A Rhenman","year":"2015","unstructured":"Rhenman A, Berglund L, Brodin T, Olovsson M, Milton K, Hadziosmanovic N, et al. Which set of embryo variables is most predictive for live birth? A prospective study in 6252 single embryo transfers to construct an embryo score for the ranking and selection of embryos. Hum Reprod. 2015;30:28\u201336.","journal-title":"Hum Reprod"},{"key":"1351_CR4","doi-asserted-by":"publisher","first-page":"729","DOI":"10.1016\/j.rbmo.2014.09.005","volume":"29","author":"MD VerMilyea","year":"2014","unstructured":"VerMilyea MD, Tan L, Anthony JT, Conaghan J, Ivani K, Gvakharia M, et al. Computer-automated time-lapse analysis results correlate with embryo implantation and clinical pregnancy: a blinded, multi-centre study. Reprod Biomed Online. 2014;29:729\u201336.","journal-title":"Reprod Biomed Online"},{"key":"1351_CR5","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1007\/s10815-014-0366-1","volume":"32","author":"MP Diamond","year":"2015","unstructured":"Diamond MP, Suraj V, Behnke EJ, Yang X, Angle MJ, Lambe-Steinmiller JC, et al. Using the Eeva Test adjunctively to traditional day 3 morphology is informative for consistent embryo assessment within a panel of embryologists with diverse experience. J Assist Reprod Genet. 2015;32:61\u20138.","journal-title":"J Assist Reprod Genet"},{"key":"1351_CR6","doi-asserted-by":"publisher","first-page":"781","DOI":"10.1016\/j.fertnstert.2019.12.004","volume":"113","author":"CL Bormann","year":"2020","unstructured":"Bormann CL, Thirumalaraju P, Kanakasabapathy MK, Kandula H, Souter I, Dimitriadis I, et al. Consistency and objectivity of automated embryo assessments using deep neural networks. Fertil Steril. 2020;113:781\u2013e71.","journal-title":"Fertil Steril"},{"key":"1351_CR7","doi-asserted-by":"publisher","first-page":"e0183328","DOI":"10.1371\/journal.pone.0183328","volume":"12","author":"L Martinez-Granados","year":"2017","unstructured":"Martinez-Granados L, Serrano M, Gonzalez-Utor A, Ortiz N, Badajoz V, Olaya E, et al. Inter-laboratory agreement on embryo classification and clinical decision: conventional morphological assessment vs. time lapse. PLoS ONE. 2017;12:e0183328.","journal-title":"PLoS ONE"},{"key":"1351_CR8","doi-asserted-by":"crossref","unstructured":"Cavalera F, Zanoni M, Merico V, Bui TTH, Belli M, Fassina L, et al. A neural network-based identification of developmentally competent or incompetent mouse fully-grown oocytes. J Vis Exp; 2018.","DOI":"10.3791\/56668"},{"key":"1351_CR9","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1016\/j.compbiomed.2019.04.030","volume":"109","author":"S Javadi","year":"2019","unstructured":"Javadi S, Mirroshandel SA. A novel deep learning method for automatic assessment of human sperm images. Comput Biol Med. 2019;109:182\u201394.","journal-title":"Comput Biol Med"},{"key":"1351_CR10","doi-asserted-by":"publisher","first-page":"557","DOI":"10.1093\/humrep\/deaa001","volume":"35","author":"M Feyeux","year":"2020","unstructured":"Feyeux M, Reignier A, Mocaer M, Lammers J, Meistermann D, Barriere P, et al. Development of automated annotation software for human embryo morphokinetics. Hum Reprod. 2020;35:557\u201364.","journal-title":"Hum Reprod"},{"key":"1351_CR11","doi-asserted-by":"publisher","first-page":"770","DOI":"10.1093\/humrep\/deaa013","volume":"35","author":"M VerMilyea","year":"2020","unstructured":"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;35:770\u201384.","journal-title":"Hum Reprod"},{"key":"1351_CR12","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1038\/s41746-019-0096-y","volume":"2","author":"P Khosravi","year":"2019","unstructured":"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.","journal-title":"NPJ Digit Med"},{"key":"1351_CR13","doi-asserted-by":"publisher","first-page":"1011","DOI":"10.1093\/humrep\/dez064","volume":"34","author":"D Tran","year":"2019","unstructured":"Tran D, Cooke S, Illingworth PJ, Gardner DK. Deep learning as a predictive tool for fetal heart pregnancy following time-lapse incubation and blastocyst transfer. Hum Reprod. 2019;34:1011\u20138.","journal-title":"Hum Reprod"},{"key":"1351_CR14","doi-asserted-by":"crossref","unstructured":"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.","DOI":"10.7554\/eLife.55301"},{"key":"1351_CR15","doi-asserted-by":"publisher","first-page":"585","DOI":"10.1016\/j.rbmo.2020.07.003","volume":"41","author":"A Chavez-Badiola","year":"2020","unstructured":"Chavez-Badiola A, Flores-Saiffe-Farias A, Mendizabal-Ruiz G, Drakeley AJ, Cohen J. Embryo Ranking Intelligent classification algorithm (ERICA): artificial intelligence clinical assistant predicting embryo ploidy and implantation. Reprod Biomed Online. 2020;41:585\u201393.","journal-title":"Reprod Biomed Online"},{"key":"1351_CR16","doi-asserted-by":"publisher","first-page":"1655","DOI":"10.1007\/s10815-021-02228-8","volume":"38","author":"CI Lee","year":"2021","unstructured":"Lee CI, Su YR, Chen CH, Chang TA, Kuo EE, Zheng WL, et al. End-to-end deep learning for recognition of ploidy status using time-lapse videos. J Assist Reprod Genet. 2021;38:1655\u201363.","journal-title":"J Assist Reprod Genet"},{"key":"1351_CR17","doi-asserted-by":"publisher","first-page":"e28","DOI":"10.1016\/S2589-7500(22)00213-8","volume":"5","author":"J Barnes","year":"2023","unstructured":"Barnes J, Brendel M, Gao VR, Rajendran S, Kim J, Li Q, et al. A non-invasive artificial intelligence approach for the prediction of human blastocyst ploidy: a retrospective model development and validation study. Lancet Digit Health. 2023;5:e28\u201340.","journal-title":"Lancet Digit Health"},{"key":"1351_CR18","doi-asserted-by":"publisher","first-page":"569","DOI":"10.1093\/humrep\/dead034","volume":"38","author":"T Bamford","year":"2023","unstructured":"Bamford T, Easter C, Montgomery S, Smith R, Dhillon-Smith RK, Barrie A, et al. A comparison of 12 machine learning models developed to predict ploidy, using a morphokinetic meta-dataset of 8147 embryos. Hum Reprod. 2023;38:569\u201381.","journal-title":"Hum Reprod"},{"key":"1351_CR19","doi-asserted-by":"publisher","first-page":"1746","DOI":"10.1093\/humrep\/deac131","volume":"37","author":"SM Diakiw","year":"2022","unstructured":"Diakiw SM, Hall JMM, VerMilyea MD, Amin J, Aizpurua J, Giardini L, 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;37:1746\u201359.","journal-title":"Hum Reprod"},{"key":"1351_CR20","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1016\/j.fertnstert.2020.07.023","volume":"115","author":"S Vagios","year":"2021","unstructured":"Vagios S, James KE, Sacha CR, Hsu JY, Dimitriadis I, Bormann CL, et al. A patient-specific model combining antimullerian hormone and body mass index as a predictor of polycystic ovary syndrome and other oligo-anovulation disorders. Fertil Steril. 2021;115:229\u201337.","journal-title":"Fertil Steril"},{"key":"1351_CR21","doi-asserted-by":"publisher","first-page":"2663","DOI":"10.1007\/s10815-021-02318-7","volume":"38","author":"VW Fitz","year":"2021","unstructured":"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;38:2663\u201370.","journal-title":"J Assist Reprod Genet"},{"key":"1351_CR22","doi-asserted-by":"publisher","first-page":"1026","DOI":"10.1016\/j.fertnstert.2020.06.006","volume":"114","author":"G Letterie","year":"2020","unstructured":"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;114:1026\u201331.","journal-title":"Fertil Steril"},{"key":"1351_CR23","doi-asserted-by":"publisher","first-page":"921","DOI":"10.1016\/j.fertnstert.2020.09.159","volume":"114","author":"CL Curchoe","year":"2020","unstructured":"Curchoe CL, Flores-Saiffe Farias A, Mendizabal-Ruiz G, Chavez-Badiola A. Evaluating predictive models in reproductive medicine. Fertil Steril. 2020;114:921\u20136.","journal-title":"Fertil Steril"},{"key":"1351_CR24","doi-asserted-by":"crossref","unstructured":"Kragh MF, Karstoft H. Embryo selection with artificial intelligence: how to evaluate and compare methods. J Assist Reprod Genet. 2021;38(7):1675\u201389.","DOI":"10.1007\/s10815-021-02254-6"},{"key":"1351_CR25","doi-asserted-by":"crossref","unstructured":"Letterie G. Three ways of knowing: the integration of clinical expertise, evidence-based medicine, and artificial intelligence in assisted reproductive technologies. J Assist Reprod Genet 2021.","DOI":"10.1007\/s10815-021-02159-4"},{"key":"1351_CR26","doi-asserted-by":"publisher","first-page":"435","DOI":"10.1016\/j.rbmo.2021.11.003","volume":"44","author":"I Dimitriadis","year":"2022","unstructured":"Dimitriadis I, Zaninovic N, Badiola AC, Bormann CL. Artificial intelligence in the embryology laboratory: a review. Reprod Biomed Online. 2022;44:435\u201348.","journal-title":"Reprod Biomed Online"},{"key":"1351_CR27","doi-asserted-by":"publisher","first-page":"934","DOI":"10.1016\/j.fertnstert.2020.10.040","volume":"114","author":"CL Curchoe","year":"2020","unstructured":"Curchoe CL, Malmsten J, Bormann C, Shafiee H, Flores-Saiffe Farias A, Mendizabal G, et al. Predictive modeling in reproductive medicine: where will the future of artificial intelligence research take us? Fertil Steril. 2020;114:934\u201340.","journal-title":"Fertil Steril"},{"key":"1351_CR28","doi-asserted-by":"publisher","first-page":"914","DOI":"10.1016\/j.fertnstert.2020.09.157","volume":"114","author":"N Zaninovic","year":"2020","unstructured":"Zaninovic N, Rosenwaks Z. Artificial intelligence in human in vitro fertilization and embryology. Fertil Steril. 2020;114:914\u201320.","journal-title":"Fertil Steril"},{"key":"1351_CR29","doi-asserted-by":"crossref","unstructured":"Sfakianoudis K, Maziotis E, Grigoriadis S, Pantou A, Kokkini G, Trypidi A et al. Reporting on the Value of Artificial Intelligence in Predicting the optimal embryo for transfer: a systematic review including data synthesis. Biomedicines 2022;10.","DOI":"10.3390\/biomedicines10030697"},{"key":"1351_CR30","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1016\/j.rbmo.2021.10.021","volume":"44","author":"DK Gardner","year":"2022","unstructured":"Gardner DK. The way to improve ART outcomes is to introduce more technologies in the laboratory. Reprod Biomed Online. 2022;44:389\u201392.","journal-title":"Reprod Biomed Online"},{"key":"1351_CR31","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1093\/humrep\/der414","volume":"27","author":"J Harper","year":"2012","unstructured":"Harper J, Magli MC, Lundin K, Barratt CL, Brison D. When and how should new technology be introduced into the IVF laboratory? Hum Reprod. 2012;27:303\u201313.","journal-title":"Hum Reprod"},{"key":"1351_CR32","doi-asserted-by":"publisher","first-page":"1155","DOI":"10.1016\/S0015-0282(00)00518-5","volume":"73","author":"DK Gardner","year":"2000","unstructured":"Gardner DK, Lane M, Stevens J, Schlenker T, Schoolcraft WB. Blastocyst score affects implantation and pregnancy outcome: towards a single blastocyst transfer. Fertil Steril. 2000;73:1155\u20138.","journal-title":"Fertil Steril"},{"key":"1351_CR33","doi-asserted-by":"publisher","first-page":"103570","DOI":"10.1016\/j.rbmo.2023.103570","volume":"48","author":"A Garcia-Belda","year":"2024","unstructured":"Garcia-Belda A, Cairo O, Martinez-Moro A, Cuadros M, Pons MC, de Mendoza MVH, et al. Considerations for future modification of the Association for the Study of Reproductive Biology embryo grading system incorporating time-lapse observations. Reprod Biomed Online. 2024;48:103570.","journal-title":"Reprod Biomed Online"},{"key":"1351_CR34","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.medre.2017.11.002","volume":"5","author":"MCPG Irene Cuevas Saiz","year":"2018","unstructured":"Irene Cuevas Saiz MCPG, Vargas MC, Mendive AD. Natalia Rives Ened\u00e1guila, Marta Moragas Solanes, Beatriz Carrasco Canal, Jos\u00e9 Teruel L\u00f3pez, Ana Busquets Bonet, M\u00aa Victoria Hurtado De Mendoza Acosta. The Embryology Interest Group: updating ASEBIR\u2019s morphological scoring system for early embryos, morulae and blastocysts. Med Reproductiva Y Embriolog\u00eda Cl\u00ednica. 2018;5:42\u201354.","journal-title":"Med Reproductiva Y Embriolog\u00eda Cl\u00ednica"},{"key":"1351_CR35","doi-asserted-by":"publisher","first-page":"1608","DOI":"10.1016\/j.fertnstert.2006.05.037","volume":"86","author":"AE Baxter Bendus","year":"2006","unstructured":"Baxter Bendus AE, Mayer JF, Shipley SK, Catherino WH. Interobserver and intraobserver variation in day 3 embryo grading. Fertil Steril. 2006;86:1608\u201315.","journal-title":"Fertil Steril"},{"key":"1351_CR36","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1007\/s10815-023-02713-2","volume":"40","author":"CL Curchoe","year":"2023","unstructured":"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;40:265\u201378.","journal-title":"J Assist Reprod Genet"},{"key":"1351_CR37","doi-asserted-by":"publisher","first-page":"103494","DOI":"10.1016\/j.compbiomed.2019.103494","volume":"115","author":"MF Kragh","year":"2019","unstructured":"Kragh MF, Rimestad J, Berntsen J, Karstoft H. Automatic grading of human blastocysts from time-lapse imaging. Comput Biol Med. 2019;115:103494.","journal-title":"Comput Biol Med"},{"key":"1351_CR38","first-page":"CD002118","volume":"5","author":"D Glujovsky","year":"2022","unstructured":"Glujovsky D, Quinteiro Retamar AM, Alvarez Sedo CR, Ciapponi A, Cornelisse S, Blake D. Cleavage-stage versus blastocyst-stage embryo transfer in assisted reproductive technology. Cochrane Database Syst Rev. 2022;5:CD002118.","journal-title":"Cochrane Database Syst Rev"},{"key":"1351_CR39","doi-asserted-by":"crossref","unstructured":"Kovacs P, Sun S, Lu Y, Romanski P, Lindheim SR. Benefits of blastocyst transfer with at least three good-quality cleavage-stage embryos in women of advanced maternal age: a retrospective analysis. J Obstet Gynaecol Can 2023:102233.","DOI":"10.1016\/j.jogc.2023.102233"},{"key":"1351_CR40","doi-asserted-by":"publisher","first-page":"347","DOI":"10.1007\/s10815-023-02997-4","volume":"41","author":"Z Liu","year":"2024","unstructured":"Liu Z, Cai J, Liu L, Ouyang L, Chen J, Yang C, et al. Does cleavage stage morphology increase the discriminatory power of prediction in blastocyst transfer outcome? J Assist Reprod Genet. 2024;41:347\u201358.","journal-title":"J Assist Reprod Genet"},{"key":"1351_CR41","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1186\/s13048-021-00838-5","volume":"14","author":"J Song","year":"2021","unstructured":"Song J, Duan C, Cai W, Xu J. Predictive value of the number of frozen blastocysts in live birth rates of the transferred fresh embryos. J Ovarian Res. 2021;14:83.","journal-title":"J Ovarian Res"},{"key":"1351_CR42","doi-asserted-by":"publisher","first-page":"2391","DOI":"10.1093\/humrep\/dead212","volume":"38","author":"H Zou","year":"2023","unstructured":"Zou H, Kemper JM, Hammond ER, Xu F, Liu G, Xue L, et al. Blastocyst quality and reproductive and perinatal outcomes: a multinational multicentre observational study. Hum Reprod. 2023;38:2391\u20139.","journal-title":"Hum Reprod"},{"key":"1351_CR43","doi-asserted-by":"publisher","first-page":"1152","DOI":"10.1016\/j.fertnstert.2010.05.042","volume":"94","author":"C Racowsky","year":"2010","unstructured":"Racowsky C, Vernon M, Mayer J, Ball GD, Behr B, Pomeroy KO, et al. Standardization of grading embryo morphology. Fertil Steril. 2010;94:1152\u20133.","journal-title":"Fertil Steril"},{"key":"1351_CR44","doi-asserted-by":"publisher","first-page":"964","DOI":"10.1093\/humrep\/der050","volume":"26","author":"S Mastenbroek","year":"2011","unstructured":"Mastenbroek S, van der Veen F, Aflatoonian A, Shapiro B, Bossuyt P, Repping S. Embryo selection in IVF. Hum Reprod. 2011;26:964\u20136.","journal-title":"Hum Reprod"},{"issue":"2","key":"1351_CR45","doi-asserted-by":"publisher","first-page":"e0262661","DOI":"10.1371\/journal.pone.0262661","volume":"17","author":"J Berntsen","year":"2022","unstructured":"Berntsen J, Rimestad J, Lassen JT, Tran D, Kragh MF. Robust and generalizable embryo selection based on artificial intelligence and time-lapse image sequences. PLoS ONE. 2022;17(2):e0262661.","journal-title":"PLoS ONE"},{"issue":"12","key":"1351_CR46","doi-asserted-by":"publisher","first-page":"3114","DOI":"10.1038\/s41591-024-03166-5","volume":"30","author":"PJ Illingworth","year":"2024","unstructured":"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(12):3114\u201320.","journal-title":"Nat Med"},{"issue":"4","key":"1351_CR47","doi-asserted-by":"publisher","first-page":"708","DOI":"10.1093\/humrep\/deac020","volume":"37","author":"A Ahlstr\u00f6m","year":"2022","unstructured":"Ahlstr\u00f6m A, Lundin K, Lind AK, Gunnarsson K, Westlander G, Park H, et al. A double-blind randomized controlled trial investigating a time-lapse algorithm for selecting Day 5 blastocysts for transfer. Hum Reprod. 2022;37(4):708\u201317.","journal-title":"Hum Reprod"}],"container-title":["Reproductive Biology and Endocrinology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12958-025-01351-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12958-025-01351-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12958-025-01351-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,31]],"date-time":"2025-01-31T04:10:03Z","timestamp":1738296603000},"score":1,"resource":{"primary":{"URL":"https:\/\/rbej.biomedcentral.com\/articles\/10.1186\/s12958-025-01351-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,31]]},"references-count":47,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1351"],"URL":"https:\/\/doi.org\/10.1186\/s12958-025-01351-w","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-5438430\/v1","asserted-by":"object"}]},"ISSN":["1477-7827"],"issn-type":[{"value":"1477-7827","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,31]]},"assertion":[{"value":"12 November 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 January 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 January 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All collected data were de-identified and retrospectively collected prior to data analysis. Agreement to share data was authorized in the Software Licensing Agreement between each participating clinic and AIVF. IRB Study approval was obtained from the local independent review boards of IVI Valencia (Spain) (ID number 1902-VLC-018-MM), Assuta Ramat Hachayal (Israel) (ID number ASMC 68\u2009\u2212\u200918), Embryolab (Greece) (ID number 05\/2019) and Clevelend Clinic (USA) (ID number 20\u2013766). In Portugal, Ireland, Germany, Korea, and the Czech Republic, the study was deemed negligible risk authorized under the terms of the signed licensing agreement. Consent to participate was not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"D.G., M.S., Y.T., Y.A., and N.L. are paid employees of AIVF. D.G. and D.S.S. have stock and share ownership of AIVF. M.M. is a member of the AIVF scientific advisory board team. C.L.C. is on the editorial board for the Journal of Assisted Reproduction and Genetics.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"16"}}