{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,3]],"date-time":"2026-11-03T06:30:39Z","timestamp":1793687439841,"version":"4.5.1"},"reference-count":41,"publisher":"Elsevier BV","issue":"1","license":[{"start":{"date-parts":[[2023,7,1]],"date-time":"2023-07-01T00:00:00Z","timestamp":1688169600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2023,7,1]],"date-time":"2023-07-01T00:00:00Z","timestamp":1688169600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T00:00:00Z","timestamp":1719792000000},"content-version":"vor","delay-in-days":366,"URL":"http:\/\/www.elsevier.com\/open-access\/userlicense\/1.0\/"}],"funder":[{"DOI":"10.13039\/100005294","name":"Massachusetts General Hospital","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100005294","id-type":"DOI","asserted-by":"publisher"}]}],"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":[[2023,7]]},"DOI":"10.1016\/j.fertnstert.2023.05.149","type":"journal-article","created":{"date-parts":[[2023,5,19]],"date-time":"2023-05-19T11:10:09Z","timestamp":1684494609000},"page":"17-23","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":86,"title":["Artificial intelligence in the in\u00a0vitro fertilization laboratory: a review of advancements over the last decade"],"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.2023.05.149_bib1","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-020-75164-9","article-title":"Evaluating the value of day 0 of an ICSI cycle on indicating laboratory outcome","volume":"10","author":"Maziotis","year":"2020","journal-title":"Sci Rep"},{"key":"10.1016\/j.fertnstert.2023.05.149_bib2","first-page":"I209","article-title":"Improving the performance of deep convolutional neural networks (CNN) in embryology using synthetic machine-generated images","volume":"35","author":"Kanakasabapathy","year":"2020","journal-title":"Hum Reprod"},{"key":"10.1016\/j.fertnstert.2023.05.149_bib3","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.rbmo.2012.09.015","article-title":"Artificial intelligence techniques for embryo and oocyte classification","volume":"26","author":"Manna","year":"2013","journal-title":"Reprod Biomed Online"},{"key":"10.1016\/j.fertnstert.2023.05.149_bib4","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1186\/s12938-021-00864-w","article-title":"Semantic segmentation of human oocyte images using deep neural networks","volume":"20","author":"Targosz","year":"2021","journal-title":"Biomed Eng Online"},{"key":"10.1016\/j.fertnstert.2023.05.149_bib5","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2021.105946","article-title":"A robust deep learning-based multiclass segmentation method for analyzing human metaphase II oocyte images","volume":"201","author":"Firuzinia","year":"2021","journal-title":"Comput Methods Programs Biomed"},{"key":"10.1016\/j.fertnstert.2023.05.149_bib6","series-title":"Laboratory manual for the examination and processing of human semen","year":"2021"},{"key":"10.1016\/j.fertnstert.2023.05.149_bib7","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.compbiomed.2017.03.029","article-title":"Automatic classification of human sperm head morphology","volume":"84","author":"Chang","year":"2017","journal-title":"Comput Biol Med"},{"key":"10.1016\/j.fertnstert.2023.05.149_bib8","doi-asserted-by":"crossref","first-page":"e41","DOI":"10.1016\/j.fertnstert.2019.07.237","article-title":"Human sperm morphology analysis using smartphone microscopy and deep learning","volume":"112","author":"Thirumalaraju","year":"2019","journal-title":"Fertil Steril"},{"key":"10.1016\/j.fertnstert.2023.05.149_bib9","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2019.103342","article-title":"Deep learning for the classification of human sperm","volume":"111","author":"Riordon","year":"2019","journal-title":"Comput Biol Med"},{"key":"10.1016\/j.fertnstert.2023.05.149_bib10","doi-asserted-by":"crossref","DOI":"10.2196\/20031","article-title":"Web- and artificial intelligence-based image recognition for sperm motility analysis: verification study","volume":"8","author":"Tsai","year":"2020","journal-title":"JMIR Med Inform"},{"key":"10.1016\/j.fertnstert.2023.05.149_bib11","doi-asserted-by":"crossref","DOI":"10.1016\/j.isci.2022.104644","article-title":"motilitAI: a machine learning framework for automatic prediction of human sperm motility","volume":"25","author":"Ottl","year":"2022","journal-title":"iScience"},{"key":"10.1016\/j.fertnstert.2023.05.149_bib12","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1038\/s42003-019-0491-6","article-title":"Deep learning-based selection of human sperm with high DNA integrity","volume":"2","author":"McCallum","year":"2019","journal-title":"Commun Biol"},{"key":"10.1016\/j.fertnstert.2023.05.149_bib13","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"},{"key":"10.1016\/j.fertnstert.2023.05.149_bib14","doi-asserted-by":"crossref","first-page":"e272","DOI":"10.1016\/j.fertnstert.2019.07.805","article-title":"Deep convolutional neural networks (CNN) for assessment and selection of normally fertilized human embryos","volume":"112","author":"Dimitriadis","year":"2019","journal-title":"Fertil Steril"},{"key":"10.1016\/j.fertnstert.2023.05.149_bib15","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1016\/j.rbmo.2020.12.008","article-title":"Cytoplasmic movements of the early human embryo: imaging and artificial intelligence to predict blastocyst development","volume":"42","author":"Coticchio","year":"2021","journal-title":"Reprod Biomed Online"},{"key":"10.1016\/j.fertnstert.2023.05.149_bib16","doi-asserted-by":"crossref","first-page":"874","DOI":"10.1016\/j.fertnstert.2019.07.015","article-title":"Noninvasive embryo selection: kinetic analysis of female and male pronuclear development to predict embryo quality and potential to produce live birth","volume":"112","author":"Otsuki","year":"2019","journal-title":"Fertil Steril"},{"key":"10.1016\/j.fertnstert.2023.05.149_bib17","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1016\/j.fertnstert.2019.12.004","article-title":"Consistency and objectivity of automated embryo assessments using deep neural networks","volume":"113","author":"Bormann","year":"2020","journal-title":"Fertil Steril"},{"key":"10.1016\/j.fertnstert.2023.05.149_bib18","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.rbmo.2021.09.022","article-title":"Inter-centre reliability in embryo grading across several IVF clinics is limited: implications for embryo selection","volume":"44","author":"Cimadomo","year":"2022","journal-title":"Reprod Biomed Online"},{"key":"10.1016\/j.fertnstert.2023.05.149_bib19","doi-asserted-by":"crossref","first-page":"2275","DOI":"10.1093\/humrep\/deac171","article-title":"Embryologist agreement when assessing blastocyst implantation probability: is data-driven prediction the solution to embryo assessment subjectivity?","volume":"37","author":"Fordham","year":"2022","journal-title":"Hum Reprod"},{"key":"10.1016\/j.fertnstert.2023.05.149_bib20","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2022.106895","article-title":"Automatic characterization of human embryos at day 4 post-insemination from time-lapse imaging using supervised contrastive learning and inductive transfer learning techniques","volume":"221","author":"Pay\u00e1","year":"2022","journal-title":"Comput Methods Programs Biomed"},{"key":"10.1016\/j.fertnstert.2023.05.149_bib21","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1038\/s41746-019-0096-y","article-title":"Deep learning enables robust assessment and selection of human blastocysts after in\u00a0vitro fertilization","volume":"2","author":"Khosravi","year":"2019","journal-title":"NPJ Digit Med"},{"key":"10.1016\/j.fertnstert.2023.05.149_bib22","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.2023.05.149_bib23","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.2023.05.149_bib24","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.2023.05.149_bib25","doi-asserted-by":"crossref","first-page":"2291","DOI":"10.1093\/humrep\/deac175","article-title":"Human blastocyst spontaneous collapse is associated with worse morphological quality and higher degeneration and aneuploidy rates: a comprehensive analysis standardized through artificial intelligence","volume":"37","author":"Cimadomo","year":"2022","journal-title":"Hum Reprod"},{"key":"10.1016\/j.fertnstert.2023.05.149_bib26","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.2023.05.149_bib27","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1016\/j.rbmo.2022.09.010","article-title":"Does embryo categorization by existing artificial intelligence, morphokinetic or morphological embryo selection models correlate with blastocyst euploidy rates?","volume":"46","author":"Kato","year":"2023","journal-title":"Reprod Biomed Online"},{"key":"10.1016\/j.fertnstert.2023.05.149_bib28","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.2023.05.149_bib29","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.2023.05.149_bib30","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.2023.05.149_bib31","doi-asserted-by":"crossref","first-page":"1011","DOI":"10.1093\/humrep\/dez064","article-title":"Deep learning as a predictive tool for fetal heart pregnancy following time-lapse incubation and blastocyst transfer","volume":"34","author":"Tran","year":"2019","journal-title":"Hum Reprod"},{"key":"10.1016\/j.fertnstert.2023.05.149_bib32","doi-asserted-by":"crossref","first-page":"1232","DOI":"10.1016\/j.fertnstert.2020.08.023","article-title":"Novel and conventional embryo parameters as input data for artificial neural networks: an artificial intelligence model applied for prediction of the implantation potential","volume":"114","author":"Bori","year":"2020","journal-title":"Fertil Steril"},{"key":"10.1016\/j.fertnstert.2023.05.149_bib33","doi-asserted-by":"crossref","DOI":"10.7554\/eLife.55301","article-title":"Performance of a deep learning based neural network in the selection of human blastocysts for implantation","volume":"9","author":"Bormann","year":"2020","journal-title":"Elife"},{"key":"10.1016\/j.fertnstert.2023.05.149_bib34","doi-asserted-by":"crossref","first-page":"770","DOI":"10.1093\/humrep\/deaa013","article-title":"Development of an artificial intelligence-based assessment model for prediction of embryo viability using static images captured by optical light microscopy during IVF","volume":"35","author":"VerMilyea","year":"2020","journal-title":"Hum Reprod"},{"key":"10.1016\/j.fertnstert.2023.05.149_bib35","doi-asserted-by":"crossref","first-page":"2663","DOI":"10.1007\/s10815-021-02318-7","article-title":"Should there be an \u201cAI\u201d in TEAM? Embryologists selection of high implantation potential embryos improves with the aid of an artificial intelligence algorithm","volume":"38","author":"Fitz","year":"2021","journal-title":"J Assist Reprod Genet"},{"key":"10.1016\/j.fertnstert.2023.05.149_bib36","doi-asserted-by":"crossref","first-page":"1519","DOI":"10.1093\/humrep\/det083","article-title":"Direct embryo tagging and identification system by attachment of biofunctionalized polysilicon barcodes to the zona pellucida of mouse embryos","volume":"28","author":"Novo","year":"2013","journal-title":"Hum Reprod"},{"key":"10.1016\/j.fertnstert.2023.05.149_bib37","doi-asserted-by":"crossref","first-page":"2343","DOI":"10.1007\/s10815-022-02585-y","article-title":"Using artificial intelligence to avoid human error in identifying embryos: a retrospective cohort study","volume":"39","author":"Hammer","year":"2022","journal-title":"J Assist Reprod Genet"},{"key":"10.1016\/j.fertnstert.2023.05.149_bib38","doi-asserted-by":"crossref","first-page":"429","DOI":"10.3390\/mi9090429","article-title":"Oocyte positional recognition for automatic manipulation in ICSI","volume":"9","author":"Saadat","year":"2018","journal-title":"Micromachines (Basel)"},{"key":"10.1016\/j.fertnstert.2023.05.149_bib39","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1007\/s10815-022-02685-9","article-title":"Advancements in the future of automating micromanipulation techniques in the IVF laboratory using deep convolutional neural networks","volume":"40","author":"Jiang","year":"2023","journal-title":"J Assist Reprod Genet"},{"key":"10.1016\/j.fertnstert.2023.05.149_bib40","doi-asserted-by":"crossref","first-page":"1641","DOI":"10.1007\/s10815-021-02198-x","article-title":"Deep learning early warning system for embryo culture conditions and embryologist performance in the ART laboratory","volume":"38","author":"Bormann","year":"2021","journal-title":"J Assist Reprod Genet"},{"key":"10.1016\/j.fertnstert.2023.05.149_bib41","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1007\/s10815-022-02649-z","article-title":"Quality assurance (QA) for monitoring the performance of assisted reproductive technology (ART) staff using artificial intelligence (AI)","volume":"40","author":"Cherouveim","year":"2023","journal-title":"J Assist Reprod Genet"}],"container-title":["Fertility and Sterility"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0015028223005204?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0015028223005204?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T08:38:34Z","timestamp":1758011914000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0015028223005204"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7]]},"references-count":41,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,7]]}},"alternative-id":["S0015028223005204"],"URL":"https:\/\/doi.org\/10.1016\/j.fertnstert.2023.05.149","relation":{},"ISSN":["0015-0282"],"issn-type":[{"value":"0015-0282","type":"print"}],"subject":[],"published":{"date-parts":[[2023,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Artificial intelligence in the in\u00a0vitro fertilization laboratory: a review of advancements over the last decade","name":"articletitle","label":"Article Title"},{"value":"Fertility and Sterility","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.fertnstert.2023.05.149","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"}]}}