{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T20:12:44Z","timestamp":1790799164292,"version":"4.1.0"},"reference-count":17,"publisher":"Elsevier BV","issue":"2","license":[{"start":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T00:00:00Z","timestamp":1690848000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T00:00:00Z","timestamp":1690848000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"content-version":"vor","delay-in-days":366,"URL":"http:\/\/www.elsevier.com\/open-access\/userlicense\/1.0\/"}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","fertstert.org","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Fertility and Sterility"],"published-print":{"date-parts":[[2023,8]]},"DOI":"10.1016\/j.fertnstert.2023.06.025","type":"journal-article","created":{"date-parts":[[2023,6,30]],"date-time":"2023-06-30T20:31:59Z","timestamp":1688157119000},"page":"228-234","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":31,"title":["Noninvasive genetic screening: current advances in artificial intelligence for embryo ploidy prediction"],"prefix":"10.1016","volume":"120","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0625-1399","authenticated-orcid":false,"given":"Victoria S.","family":"Jiang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Charles L.","family":"Bormann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.fertnstert.2023.06.025_bib1","doi-asserted-by":"crossref","first-page":"1071","DOI":"10.1016\/j.fertnstert.2019.07.1346","article-title":"Preimplantation genetic testing for aneuploidy versus morphology as selection criteria for single frozen-thawed embryo transfer in good-prognosis patients: a multicenter randomized clinical trial","volume":"112","author":"Munn\u00e9","year":"2019","journal-title":"Fertil Steril"},{"key":"10.1016\/j.fertnstert.2023.06.025_bib2","doi-asserted-by":"crossref","first-page":"819","DOI":"10.1007\/s10815-019-01437-6","article-title":"Current status of spent embryo media research for preimplantation genetic testing","volume":"36","author":"Belandres","year":"2019","journal-title":"J Assist Reprod Genet"},{"key":"10.1016\/j.fertnstert.2023.06.025_bib3","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1186\/s12958-021-00714-3","article-title":"Validation of preimplantation genetic tests for aneuploidy (PGT-A) with DNA from spent culture media (SCM): concordance assessment and implication","volume":"19","author":"Yin","year":"2021","journal-title":"Reprod Biol Endocrinol"},{"key":"10.1016\/j.fertnstert.2023.06.025_bib4","doi-asserted-by":"crossref","DOI":"10.3389\/fphys.2022.794210","article-title":"Ploidy testing of blastocoel fluid for screening may be technically challenging and more invasive than that of spent cell culture media","volume":"13","author":"Shi","year":"2022","journal-title":"Front Physiol"},{"key":"10.1016\/j.fertnstert.2023.06.025_bib5","doi-asserted-by":"crossref","first-page":"738","DOI":"10.1016\/j.fertnstert.2021.11.029","article-title":"Machine learning for prediction of euploidy in human embryos: in search of the best-performing model and predictive features","volume":"117","author":"De Gheselle","year":"2022","journal-title":"Fertil Steril"},{"key":"10.1016\/j.fertnstert.2023.06.025_bib6","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1186\/s12884-022-04569-3","article-title":"Nomogram based on clinical and laboratory characteristics of euploid embryos using the data in PGT-A: a euploid-prediction model","volume":"22","author":"Liu","year":"2022","journal-title":"BMC Pregnancy Childbirth"},{"key":"10.1016\/j.fertnstert.2023.06.025_bib7","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1093\/humrep\/dead034","article-title":"A comparison of 12 machine learning models developed to predict ploidy, using a morphokinetic meta-dataset of 8147 embryos","volume":"38","author":"Bamford","year":"2023","journal-title":"Hum Reprod"},{"key":"10.1016\/j.fertnstert.2023.06.025_bib8","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1016\/j.rbmo.2020.07.003","article-title":"Embryo Ranking Intelligent Classification Algorithm (ERICA): artificial intelligence clinical assistant predicting embryo ploidy and implantation","volume":"41","author":"Chavez-Badiola","year":"2020","journal-title":"Reprod Biomed Online"},{"key":"10.1016\/j.fertnstert.2023.06.025_bib9","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1007\/s10815-022-02707-6","article-title":"The use of voting ensembles to improve the accuracy of deep neural networks as a non-invasive method to predict embryo ploidy status","volume":"40","author":"Jiang","year":"2023","journal-title":"J Assist Reprod Genet"},{"key":"10.1016\/j.fertnstert.2023.06.025_bib10","doi-asserted-by":"crossref","first-page":"1746","DOI":"10.1093\/humrep\/deac131","article-title":"Development of an artificial intelligence model for predicting the likelihood of human embryo euploidy based on blastocyst images from multiple imaging systems during IVF","volume":"37","author":"Diakiw","year":"2022","journal-title":"Hum Reprod"},{"key":"10.1016\/j.fertnstert.2023.06.025_bib11","doi-asserted-by":"crossref","first-page":"e28","DOI":"10.1016\/S2589-7500(22)00213-8","article-title":"A non-invasive artificial intelligence approach for the prediction of human blastocyst ploidy: a retrospective model development and validation study","volume":"5","author":"Barnes","year":"2023","journal-title":"Lancet Digit Health"},{"key":"10.1016\/j.fertnstert.2023.06.025_bib12","doi-asserted-by":"crossref","first-page":"1655","DOI":"10.1007\/s10815-021-02228-8","article-title":"End-to-end deep learning for recognition of ploidy status using time-lapse videos","volume":"38","author":"Lee","year":"2021","journal-title":"J Assist Reprod Genet"},{"key":"10.1016\/j.fertnstert.2023.06.025_bib13","doi-asserted-by":"crossref","first-page":"2322","DOI":"10.1038\/s41598-023-29319-z","article-title":"Development of an artificial intelligence based model for predicting the euploidy of blastocysts in PGT-A treatments","volume":"13","author":"Yuan","year":"2023","journal-title":"Sci Rep"},{"key":"10.1016\/j.fertnstert.2023.06.025_bib14","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1186\/s12958-021-00864-4","article-title":"An artificial intelligence model (euploid prediction algorithm) can predict embryo ploidy status based on time-lapse data","volume":"19","author":"Huang","year":"2021","journal-title":"Reprod Biol Endocrinol"},{"key":"10.1016\/j.fertnstert.2023.06.025_bib15","doi-asserted-by":"crossref","first-page":"643","DOI":"10.1016\/j.rbmo.2022.06.007","article-title":"Can the combination of time-lapse parameters and clinical features predict embryonic ploidy status or implantation?","volume":"45","author":"Zou","year":"2022","journal-title":"Reprod Biomed Online"},{"key":"10.1016\/j.fertnstert.2023.06.025_bib16","doi-asserted-by":"crossref","first-page":"1203","DOI":"10.1016\/j.fertnstert.2022.02.015","article-title":"Noninvasive embryo evaluation and selection by time-lapse monitoring vs. conventional morphologic assessment in women undergoing in\u00a0vitro fertilization\/intracytoplasmic sperm injection: a single-center randomized controlled study","volume":"117","author":"Meng","year":"2022","journal-title":"Fertil Steril"},{"key":"10.1016\/j.fertnstert.2023.06.025_bib17","doi-asserted-by":"crossref","first-page":"1007","DOI":"10.1016\/j.fertnstert.2017.08.004","article-title":"Abnormally fertilized oocytes can result in healthy live births: improved genetic technologies for preimplantation genetic testing can be used to rescue viable embryos in in\u00a0vitro fertilization cycles","volume":"108","author":"Capalbo","year":"2017","journal-title":"Fertil Steril"}],"container-title":["Fertility and Sterility"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0015028223006283?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0015028223006283?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T08:38:49Z","timestamp":1758011929000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0015028223006283"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8]]},"references-count":17,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,8]]}},"alternative-id":["S0015028223006283"],"URL":"https:\/\/doi.org\/10.1016\/j.fertnstert.2023.06.025","relation":{},"ISSN":["0015-0282"],"issn-type":[{"value":"0015-0282","type":"print"}],"subject":[],"published":{"date-parts":[[2023,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Noninvasive genetic screening: current advances in artificial intelligence for embryo ploidy prediction","name":"articletitle","label":"Article Title"},{"value":"Fertility and Sterility","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.fertnstert.2023.06.025","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a92023 American Society for Reproductive Medicine, Published by Elsevier Inc.","name":"copyright","label":"Copyright"}]}}