{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T12:06:37Z","timestamp":1790769997191,"version":"4.1.0"},"reference-count":24,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2018,3,4]],"date-time":"2018-03-04T00:00:00Z","timestamp":1520121600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["obgyn.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Ultrasound in Obstet &amp; Gyne"],"published-print":{"date-parts":[[2018,4]]},"abstract":"<jats:title>ABSTRACT<\/jats:title><jats:sec><jats:title>Objective<\/jats:title><jats:p>To estimate the risk of fetal trisomy 21 (T21) and other chromosomal abnormalities (OCA) at 11\u201313\u2009weeks' gestation using computational intelligence classification methods.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>As a first step, a training dataset consisting of 72\u2009054 euploid pregnancies, 295 cases of T21 and 305 cases of OCA was used to train an artificial neural network. Then, a two\u2010stage approach was used for stratification of risk and diagnosis of cases of aneuploidy in the blind set. In Stage 1, using four markers, pregnancies in the blind set were classified into no risk and risk. No\u2010risk pregnancies were not examined further, whereas the risk pregnancies were forwarded to Stage 2 for further examination. In Stage 2, using seven markers, pregnancies were classified into three types of risk, namely no risk, moderate risk and high risk.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Of 36\u2009328 unknown to the system pregnancies (blind set), 17\u2009512 euploid, two T21 and 18 OCA were classified as no risk in Stage 1. The remaining 18\u2009796 cases were forwarded to Stage 2, of which 7895 euploid, two T21 and two OCA cases were classified as no risk, 10\u2009464 euploid, 83\u2009T21 and 61 OCA as moderate risk and 187 euploid, 50\u2009T21 and 52 OCA as high risk. The sensitivity and the specificity for T21 in Stage 2 were 97.1% and 99.5%, respectively, and the false\u2010positive rate from Stage 1 to Stage 2 was reduced from 51.4% to \u223c1%, assuming that the cell\u2010free DNA test could identify all euploid and aneuploid cases.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>We propose a method for early diagnosis of chromosomal abnormalities that ensures that most T21 cases are classified as high risk at any stage. At the same time, the number of euploid cases subjected to invasive or cell\u2010free DNA examinations was minimized through a routine procedure offered in two stages. Our method is minimally invasive and of relatively low cost, highly effective at T21 identification and it performs better than do other existing statistical methods. Copyright \u00a9 2017 ISUOG. Published by John Wiley &amp; Sons Ltd.<\/jats:p><\/jats:sec>","DOI":"10.1002\/uog.17558","type":"journal-article","created":{"date-parts":[[2017,6,22]],"date-time":"2017-06-22T15:42:36Z","timestamp":1498146156000},"page":"503-508","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Two\u2010stage approach for risk estimation of fetal trisomy 21 and other aneuploidies using computational intelligence systems"],"prefix":"10.1002","volume":"51","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0730-2314","authenticated-orcid":false,"given":"A. C.","family":"Neocleous","sequence":"first","affiliation":[{"name":"Department of Intelligent Systems Group, Johann Bernoulli Institute for Mathematics and Computer Science University of Groningen  Groningen The Netherlands"},{"name":"Department of Computer Science University of Cyprus  Nicosia Cyprus"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5856-6072","authenticated-orcid":false,"given":"A.","family":"Syngelaki","sequence":"additional","affiliation":[{"name":"Harris Birthright Research Centre for Fetal Medicine King's College Hospital  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"K. H.","family":"Nicolaides","sequence":"additional","affiliation":[{"name":"Harris Birthright Research Centre for Fetal Medicine King's College Hospital  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"C. N.","family":"Schizas","sequence":"additional","affiliation":[{"name":"Department of Computer Science University of Cyprus  Nicosia Cyprus"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2018,3,4]]},"reference":[{"key":"e_1_2_6_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/pd.2637"},{"key":"e_1_2_6_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/uog.17283"},{"key":"e_1_2_6_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/uog.6318"},{"key":"e_1_2_6_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/uog.6330"},{"key":"e_1_2_6_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/uog.6264"},{"key":"e_1_2_6_7_1","doi-asserted-by":"publisher","DOI":"10.1002\/uog.17484"},{"key":"e_1_2_6_8_1","doi-asserted-by":"publisher","DOI":"10.2174\/157488407781668811"},{"key":"e_1_2_6_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/51.816244"},{"key":"e_1_2_6_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/4233.640655"},{"key":"e_1_2_6_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0303-2647(96)01668-1"},{"key":"e_1_2_6_12_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1471-0528.1975.tb00710.x"},{"key":"e_1_2_6_13_1","doi-asserted-by":"publisher","DOI":"10.3233\/IDA-2002-6504"},{"key":"e_1_2_6_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2011.11.005"},{"key":"e_1_2_6_15_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0146413"},{"key":"e_1_2_6_16_1","first-page":"818","article-title":"The cost of type II diabetes mellitus: a machine learning perspective","author":"Sheikhi G","year":"2016","journal-title":"XIV Mediterranean Conference on Medical and Biological Engineering and Computing"},{"key":"e_1_2_6_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2015.06.005"},{"key":"e_1_2_6_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2007.12.031"},{"key":"e_1_2_6_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-39796-2_41"},{"key":"e_1_2_6_20_1","doi-asserted-by":"publisher","DOI":"10.1002\/9781118646106"},{"key":"e_1_2_6_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2015.2461436"},{"key":"e_1_2_6_22_1","first-page":"179","article-title":"Addressing the curse of imbalanced training sets: one\u2010sided selection","volume":"97","author":"Kubat M","year":"1997","journal-title":"ICML"},{"key":"e_1_2_6_23_1","first-page":"281","article-title":"Some methods for classification and analysis of multivariate observations","volume":"1","author":"MacQueen J.","year":"1967","journal-title":"Proc Fifth Berkeley Symp Math Stat Prob"},{"key":"e_1_2_6_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2016.2608859"},{"key":"e_1_2_6_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2015.2462744"}],"container-title":["Ultrasound in Obstetrics &amp; Gynecology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.wiley.com\/onlinelibrary\/tdm\/v1\/articles\/10.1002%2Fuog.17558","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/obgyn.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/uog.17558","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,24]],"date-time":"2023-09-24T07:21:46Z","timestamp":1695540106000},"score":1,"resource":{"primary":{"URL":"https:\/\/obgyn.onlinelibrary.wiley.com\/doi\/10.1002\/uog.17558"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,3,4]]},"references-count":24,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2018,4]]}},"alternative-id":["10.1002\/uog.17558"],"URL":"https:\/\/doi.org\/10.1002\/uog.17558","archive":["Portico"],"relation":{},"ISSN":["0960-7692","1469-0705"],"issn-type":[{"value":"0960-7692","type":"print"},{"value":"1469-0705","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,3,4]]},"assertion":[{"value":"2016-07-02","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2017-06-12","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2018-03-04","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}