{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T17:48:59Z","timestamp":1789753739831,"version":"4.0.1"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T00:00:00Z","timestamp":1638316800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,12,13]],"date-time":"2021-12-13T00:00:00Z","timestamp":1639353600000},"content-version":"vor","delay-in-days":12,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100010909","name":"young scientists fund","doi-asserted-by":"publisher","award":["81801531"],"award-info":[{"award-number":["81801531"]}],"id":[{"id":"10.13039\/501100010909","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Reprod Biol Endocrinol"],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>For the association between time-lapse technology (TLT) and embryo ploidy status, there has not yet been fully understood. TLT has the characteristics of large amount of data and non-invasiveness. If we want to accurately predict embryo ploidy status from TLT, artificial intelligence (AI) technology is a good choice. However, the current work of AI in this field needs to be strengthened.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A total of 469 preimplantation genetic testing (PGT) cycles and 1803 blastocysts from April 2018 to November 2019 were included in the study. All embryo images are captured during 5 or 6 days after fertilization before biopsy by time-lapse microscope system. All euploid embryos or aneuploid embryos are used as data sets. The data set is divided into training set, validation set and test set. The training set is mainly used for model training, the validation set is mainly used to adjust the hyperparameters of the model and the preliminary evaluation of the model, and the test set is used to evaluate the generalization ability of the model. For better verification, we used data other than the training data for external verification. A total of 155 PGT cycles from December 2019 to December 2020 and 523 blastocysts were included in the verification process.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The euploid prediction algorithm (EPA) was able to predict euploid on the testing dataset with an area under curve (AUC) of 0.80.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The TLT incubator has gradually become the choice of reproductive centers. Our AI model named EPA that can predict embryo ploidy well based on TLT data. We hope that this system can serve all in vitro fertilization and embryo transfer (IVF-ET) patients in the future, allowing embryologists to have more non-invasive aids when selecting the best embryo to transfer.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12958-021-00864-4","type":"journal-article","created":{"date-parts":[[2021,12,13]],"date-time":"2021-12-13T01:02:27Z","timestamp":1639357347000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":70,"title":["An artificial intelligence model (euploid prediction algorithm) can predict embryo ploidy status based on time-lapse data"],"prefix":"10.1186","volume":"19","author":[{"given":"Bo","family":"Huang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Tan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhou","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3758-9640","authenticated-orcid":false,"given":"Lei","family":"Jin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,12,13]]},"reference":[{"key":"864_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":"864_CR2","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1016\/j.fertnstert.2014.12.116","volume":"103","author":"RL Krisher","year":"2015","unstructured":"Krisher RL, Schoolcraft WB, Katz-Jaffe MG. Omics as a window to view embryo viability. Fertil Steril. 2015;103:333\u201341.","journal-title":"Fertil Steril"},{"key":"864_CR3","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1210\/er.2014-1046","volume":"36","author":"GA Thouas","year":"2015","unstructured":"Thouas GA, Francisco D, Green MP, Felipe V, Carlos S, Gardner DK. Soluble ligands and their receptors in human embryo development and implantation. Endocrine Reviews. 2015;36:92\u2013130.","journal-title":"Endocrine Reviews"},{"issue":"Suppl 1","key":"864_CR4","first-page":"S125-130","volume":"68","author":"MG Katz-Jaffe","year":"2007","unstructured":"Katz-Jaffe MG, Gardner DK. Embryology in the era of proteomics. Theriogenology. 2007;68(Suppl 1):S125-130.","journal-title":"Theriogenology"},{"key":"864_CR5","doi-asserted-by":"publisher","first-page":"1073","DOI":"10.1016\/j.fertnstert.2012.12.038","volume":"99","author":"MG Katz-Jaffe","year":"2013","unstructured":"Katz-Jaffe MG, McReynolds S. Embryology in the era of proteomics. Fertil Steril. 2013;99:1073\u20137.","journal-title":"Fertil Steril"},{"key":"864_CR6","doi-asserted-by":"publisher","first-page":"2162","DOI":"10.1093\/humrep\/dey314","volume":"33","author":"C Farra","year":"2018","unstructured":"Farra C, Choucair F, Awwad J. Non-invasive pre-implantation genetic testing of human embryos: an emerging concept. Human Reproduction. 2018;33:2162\u20137.","journal-title":"Human Reproduction"},{"issue":"3","key":"864_CR7","doi-asserted-by":"publisher","first-page":"624","DOI":"10.1016\/j.fertnstert.2013.04.039","volume":"2013","author":"RT Scott","year":"2013","unstructured":"Scott RT, Upham KM, Forman EJ, Zhao, Treff NR. Cleavage-stage biopsy significantly impairs human embryonic implantation potential while blastocyst biopsy does not: a randomized and paired clinical trial. Fertil Steril. 2013;2013(3):624\u201330.","journal-title":"Fertil Steril"},{"key":"864_CR8","doi-asserted-by":"publisher","first-page":"1481-1489.e1410","DOI":"10.1016\/j.fertnstert.2012.08.016","volume":"98","author":"M Meseguer","year":"2012","unstructured":"Meseguer M, Rubio I, Cruz M, Basile N, Marcos J, Requena A. Embryo incubation and selection in a time-lapse monitoring system improves pregnancy outcome compared with a standard incubator: a retrospective cohort study. Fertil Steril. 2012;98:1481-1489.e1410.","journal-title":"Fertil Steril"},{"key":"864_CR9","doi-asserted-by":"publisher","first-page":"412-419.e415","DOI":"10.1016\/j.fertnstert.2013.04.021","volume":"100","author":"J Conaghan","year":"2013","unstructured":"Conaghan J, Chen AA, Willman SP, Ivani K, Chenette PE, Boostanfar R, Baker VL, Adamson GD, Abusief ME, Gvakharia M. Improving embryo selection using a computer-automated time-lapse image analysis test plus day 3 morphology: results from a prospective multicenter trial. Fertil Steril. 2013;100:412-419.e415.","journal-title":"Fertil Steril"},{"key":"864_CR10","doi-asserted-by":"crossref","unstructured":"Apter S, Ebner T, Freour T, Yves G, Borut K, Nathalie LC, Monica M, Marcos M, Debbie M. Good practice recommendations for the use of time-lapse technology\u2020. Hum Reprod Open. 2020;2:1\u201326.","DOI":"10.1093\/hropen\/hoaa008"},{"key":"864_CR11","doi-asserted-by":"crossref","unstructured":"Ciray N, Campbell A, Inge EA, Jes\u00fas A, Sandrine C, Marga E, Shabana S. Proposed guidelines on the nomenclature and annotation of dynamic human embryo monitoring by a time-lapse user group. Hum Reprod. 2014:2650\u201360.","DOI":"10.1093\/humrep\/deu278"},{"key":"864_CR12","doi-asserted-by":"publisher","first-page":"1169","DOI":"10.1016\/j.fertnstert.2019.01.025","volume":"111","author":"E Somigliana","year":"2019","unstructured":"Somigliana E, Busnelli A, Paffoni A, Vigano P, Riccaboni A, Rubio C, Capalbo A. Cost-effectiveness of preimplantation genetic testing for aneuploidies. Fertil Steril. 2019;111:1169\u201376.","journal-title":"Fertil Steril"},{"key":"864_CR13","doi-asserted-by":"publisher","first-page":"353","DOI":"10.1016\/j.fertnstert.2018.06.002","volume":"110","author":"Z Rosenwaks","year":"2018","unstructured":"Rosenwaks Z, Handyside AH, Fiorentino F, Gleicher N, Paulson RJ, Schattman GL, Scott RT Jr., Summers MC, Treff NR, Xu K. The pros and cons of preimplantation genetic testing for aneuploidy: clinical and laboratory perspectives. Fertil Steril. 2018;110:353\u201361.","journal-title":"Fertil Steril"},{"key":"864_CR14","doi-asserted-by":"crossref","unstructured":"Griffin DK, Cagri O. Chromosomal analysis in IVF: just how useful is it? Reproduction. 2018;156:29\u201350.","DOI":"10.1530\/REP-17-0683"},{"key":"864_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10815-020-01696-8","volume":"37","author":"R Orvieto","year":"2020","unstructured":"Orvieto R, Gleicher N. Preimplantation genetic testing for aneuploidy (PGT-A)\u2014finally revealed. Journal of assisted reproduction and genetics. 2020;37:1\u20134.","journal-title":"Journal of assisted reproduction and genetics"},{"key":"864_CR16","doi-asserted-by":"publisher","first-page":"380","DOI":"10.1016\/j.rbmo.2018.01.001","volume":"36","author":"A Reignier","year":"2018","unstructured":"Reignier A, Lammers J, Barriere P, Freour T. Can time-lapse parameters predict embryo ploidy? A systematic review. Reprod Biomed Online. 2018;36:380\u20137.","journal-title":"Reprod Biomed Online"},{"key":"864_CR17","doi-asserted-by":"publisher","first-page":"722","DOI":"10.1016\/j.fertnstert.2017.10.002","volume":"108","author":"N Zaninovic","year":"2017","unstructured":"Zaninovic N, Irani M, Meseguer M. Assessment of embryo morphology and developmental dynamics by time-lapse microscopy: is there a relation to implantation and ploidy? Fertil Steril. 2017;108:722\u20139.","journal-title":"Fertil Steril"},{"key":"864_CR18","doi-asserted-by":"publisher","first-page":"1545","DOI":"10.1007\/s10815-018-1266-6","volume":"35","author":"S Mara","year":"2018","unstructured":"Mara S, Konstantinos S, Evangelos M, Nikolaos A, Anna R, George A, Panagiotis B, Stamatis B, Agni P, Konstantinos P. Are computational applications the \u201ccrystal ball\u201d in the IVF laboratory? The evolution from mathematics to artificial intelligence. J Assist Reprod Genet. 2018;35:1545\u201357.","journal-title":"J Assist Reprod Genet"},{"key":"864_CR19","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":"864_CR20","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, Sigaras A, Lavery S, Cooper LAD, Hickman C. Deep learning enables robust assessment and selection of human blastocysts after in vitro fertilization. NPJ Digital Med. 2019;2:21.","journal-title":"NPJ Digital Med"},{"key":"864_CR21","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-Far\u00edas A, Mendizabal-Ruiz G, Drakeley AJ, Cohen J. Embryo Ranking Intelligent Classification Algorithm (ERICA), an artificial intelligence clinical assistant with embryo ploidy and implantation predicting capabilities. Reprod Biomed Online. 2020;41:585\u201393.","journal-title":"Reprod Biomed Online"},{"key":"864_CR22","first-page":"1","volume":"1","author":"L Bori","year":"2020","unstructured":"Bori L, Dominguez F, Fernandez EI, Gallego RD, Meseguer M. An artificial intelligence model based on the proteomic profile of euploid embryos and time-lapse images: a preliminary study. Reprod Biomed Online. 2020;1:1.","journal-title":"Reprod Biomed Online"},{"key":"864_CR23","first-page":"1432-1437 e1431","volume":"103","author":"B Huang","year":"2015","unstructured":"Huang B, Qian K, Li Z, Yue J, Yang W, Zhu G, Zhang H. Neonatal outcomes after early rescue intracytoplasmic sperm injection: an analysis of a 5-year period. Fertil Steril. 2015;103:1432-1437 e1431.","journal-title":"Fertil Steril"},{"key":"864_CR24","doi-asserted-by":"publisher","first-page":"1691","DOI":"10.1093\/humrep\/deab064","volume":"36","author":"L Wu","year":"2021","unstructured":"Wu L, Jin L, Chen W, Liu JM, Hu J, Yu Q, Ren XL, Huang B, He H. The true incidence of chromosomal mosaicism after preimplantation genetic testing is much lower than that indicated by trophectoderm biopsy. Hum Reprod. 2021;36:1691\u2013701.","journal-title":"Hum Reprod"},{"key":"864_CR25","doi-asserted-by":"crossref","unstructured":"Alpha Scientists In Reproductive Medicine and Eshre Special Interest Group Embryology. The Istanbul consensus workshop on embryo assessment: proceedings of an expert meeting. Hum Reprod. 2011;22:632\u201346.","DOI":"10.1016\/j.rbmo.2011.02.001"},{"key":"864_CR26","doi-asserted-by":"crossref","unstructured":"Hara K, Kataoka H, Satoh Y. Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet? In CVPR2018. 2018.","DOI":"10.1109\/CVPR.2018.00685"},{"key":"864_CR27","doi-asserted-by":"crossref","unstructured":"Adolfsson E, Andershed AN. Morphology vs morphokinetics: a retrospective comparison of inter-observer and intra-observer agreement between embryologists on blastocysts with known implantation outcome. JBRA Assist Reprod. 2018;22:228\u201337.","DOI":"10.5935\/1518-0557.20180042"},{"key":"864_CR28","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1016\/j.artmed.2008.07.017","volume":"46","author":"VL Patel","year":"2009","unstructured":"Patel VL, Shortliffe EH, Stefanelli M, Szolovits P, Berthold MR, Bellazzi R, Abu-Hanna A. The coming of age of artificial intelligence in medicine. Artif Intell Med. 2009;46:5\u201317.","journal-title":"Artif Intell Med"},{"key":"864_CR29","doi-asserted-by":"publisher","first-page":"699","DOI":"10.1016\/j.fertnstert.2013.12.005","volume":"101","author":"N Basile","year":"2014","unstructured":"Basile N, Nogales Mdel C, Bronet F, Florensa M, Riqueiros M, Rodrigo L, Garcia-Velasco J, Meseguer M. Increasing the probability of selecting chromosomally normal embryos by time-lapse morphokinetics analysis. Fertil Steril. 2014;101:699\u2013704.","journal-title":"Fertil Steril"},{"key":"864_CR30","doi-asserted-by":"crossref","first-page":"229-235 e222","DOI":"10.1016\/j.fertnstert.2016.09.019","volume":"107","author":"M Del Carmen Nogales","year":"2017","unstructured":"Del Carmen Nogales M, Bronet F, Basile N, Martinez EM, Linan A, Rodrigo L, Meseguer M. Type of chromosome abnormality affects embryo morphology dynamics. Fertil Steril. 2017;107:229-235 e222.","journal-title":"Fertil Steril"},{"key":"864_CR31","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1007\/s10815-014-0372-3","volume":"32","author":"M Chawla","year":"2015","unstructured":"Chawla M, Fakih M, Shunnar A, Bayram A, Hellani A, Perumal V, Divakaran J, Budak E. Morphokinetic analysis of cleavage stage embryos and its relationship to aneuploidy in a retrospective time-lapse imaging study. J Assist Reprod Genet. 2015;32:69\u201375.","journal-title":"J Assist Reprod Genet"},{"key":"864_CR32","doi-asserted-by":"publisher","first-page":"112","DOI":"10.4103\/0974-1208.183511","volume":"9","author":"DV Patel","year":"2016","unstructured":"Patel DV, Shah PB, Kotdawala AP, Herrero J, Rubio I, Banker MR. Morphokinetic behavior of euploid and aneuploid embryos analyzed by time-lapse in embryoscope. J Hum Reprod Sci. 2016;9:112\u20138.","journal-title":"J Hum Reprod Sci"},{"key":"864_CR33","doi-asserted-by":"publisher","first-page":"608-614 e602","DOI":"10.1016\/j.fertnstert.2016.04.041","volume":"106","author":"H Balakier","year":"2016","unstructured":"Balakier H, Sojecki A, Motamedi G, Librach C. Impact of multinucleated blastomeres on embryo developmental competence, morphokinetics, and aneuploidy. Fertil Steril. 2016;106:608-614 e602.","journal-title":"Fertil Steril"},{"key":"864_CR34","first-page":"2245","volume":"10","author":"MM Giulia","year":"2016","unstructured":"Giulia MM, Alessandro C, Teresa R, Alessandra R, Valentina C, Filomena S, Francesca S, Francesco F, Teresa VM, Ermanno G. Correlation between aneuploidy, standard morphology evaluation and morphokinetic development in 1730 biopsied blastocysts: a consecutive case series study. Hum Reprod. 2016;10:2245.","journal-title":"Hum Reprod"},{"key":"864_CR35","doi-asserted-by":"publisher","first-page":"477","DOI":"10.1016\/j.rbmo.2013.02.006","volume":"26","author":"A Campbell","year":"2013","unstructured":"Campbell A, Fishel S, Bowman N, Duffy S, Sedler M, Hickman CFL. Modelling a risk classification of aneuploidy in human embryos using non-invasive morphokinetics. Reprod Biomed Online. 2013;26:477\u201385.","journal-title":"Reprod Biomed Online"},{"key":"864_CR36","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1016\/j.rbmo.2013.04.013","volume":"27","author":"A Campbell","year":"2013","unstructured":"Campbell A, Fishel S, Bowman N, Duffy S, Sedler M, Thornton S. Retrospective analysis of outcomes after IVF using an aneuploidy risk model derived from time-lapse imaging without PGS. Reprod Biomed Online. 2013;27:140\u20136.","journal-title":"Reprod Biomed Online"},{"key":"864_CR37","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1016\/j.rbmo.2013.11.016","volume":"28","author":"A Campbell","year":"2014","unstructured":"Campbell A, Fishel S, Laegdsmand M. Aneuploidy is a key causal factor of delays in blastulation: author response to \u2018A cautionary note against aneuploidy risk assessment using time-lapse imaging\u2019. Reprod Biomed Online. 2014;28:279\u201383.","journal-title":"Reprod Biomed Online"},{"key":"864_CR38","doi-asserted-by":"publisher","first-page":"3215","DOI":"10.1093\/humrep\/det366","volume":"28","author":"L Sundvall","year":"2013","unstructured":"Sundvall L, Ingerslev HJ, Breth Knudsen U, Kirkegaard K. Inter- and intra-observer variability of time-lapse annotations. Human Reproduction. 2013;28:3215\u201321.","journal-title":"Human Reproduction"},{"key":"864_CR39","doi-asserted-by":"crossref","unstructured":"Harper J, Jackson E, Sermon K, Aitken RJ, Harbottle S, Mocanu E, Hardarson T, Mathur R, Viville S, Vail A. Adjuncts in the IVF laboratory: where is the evidence for \u2018add-on\u2019 interventions? Hum Reprod.\u00a02017;3:485\u201391.","DOI":"10.1093\/humrep\/dex004"},{"key":"864_CR40","doi-asserted-by":"crossref","unstructured":"Curchoe CL, Bormann CL. Artificial intelligence and machine learning for human reproduction and embryology presented at ASRM and ESHRE 2018. J Assist Reprod Genet.\u00a02019;4:591\u2013600.","DOI":"10.1007\/s10815-019-01408-x"}],"container-title":["Reproductive Biology and Endocrinology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12958-021-00864-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12958-021-00864-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12958-021-00864-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,13]],"date-time":"2021-12-13T01:04:46Z","timestamp":1639357486000},"score":1,"resource":{"primary":{"URL":"https:\/\/rbej.biomedcentral.com\/articles\/10.1186\/s12958-021-00864-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12]]},"references-count":40,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["864"],"URL":"https:\/\/doi.org\/10.1186\/s12958-021-00864-4","relation":{},"ISSN":["1477-7827"],"issn-type":[{"value":"1477-7827","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12]]},"assertion":[{"value":"20 July 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 November 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 December 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"It was approved by the Ethical Committee of Reproductive Medicine Center, Tongji Hospital, Tongji Medicine College, Huazhong University of Science and Technology (No. S097).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors report no financial or commercial conflicts of interest.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"185"}}