{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T12:06:45Z","timestamp":1790770005269,"version":"4.1.0"},"reference-count":31,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2019,7,9]],"date-time":"2019-07-09T00:00:00Z","timestamp":1562630400000},"content-version":"vor","delay-in-days":8,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["obgyn.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Ultrasound in Obstet &amp;amp; Gyne"],"published-print":{"date-parts":[[2019,7]]},"abstract":"<jats:title>ABSTRACT<\/jats:title><jats:sec><jats:title>Objective<\/jats:title><jats:p>To evaluate the application of artificial intelligence (AI), i.e. deep learning and other machine\u2010learning techniques, to amniotic fluid (AF) metabolomics and proteomics, alone and in combination with sonographic, clinical and demographic factors, in the prediction of perinatal outcome in asymptomatic pregnant women with short cervical length (CL).<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>AF samples, which had been obtained in the second trimester from asymptomatic women with short CL (&lt;\u200915 mm) identified on transvaginal ultrasound, were analyzed. CL, funneling and the presence of AF \u2018sludge\u2019 were assessed in all cases close to the time of amniocentesis. A combination of liquid chromatography coupled with mass spectrometry and proton nuclear magnetic resonance spectroscopy\u2010based metabolomics, as well as targeted proteomics analysis, including chemokines, cytokines and growth factors, was performed on the AF samples. To determine the robustness of the markers, we used six different machine\u2010learning techniques, including deep learning, to predict preterm delivery &lt;\u200934 weeks, latency period prior to delivery  &lt;\u200928 days after amniocentesis and requirement for admission to a neonatal intensive care unit (NICU). Omics biomarkers were evaluated alone and in combination with standard sonographic, clinical and demographic factors to predict outcome. Predictive accuracy was assessed using the area under the receiver\u2013operating characteristics curve (AUC) with 95% CI, sensitivity and specificity.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Of the 32 patients included in the study, complete omics, demographic and clinical data and outcome information were available for 26. Of these, 11 (42.3%) patients delivered \u2265\u200934 weeks, while 15 (57.7%) delivered &lt;\u200934 weeks. There was no statistically significant difference in CL between these two groups (mean\u2009\u00b1\u2009SD, 11.2\u2009\u00b1\u20094.4\u2009mm <jats:italic>vs<\/jats:italic> 8.9\u2009\u00b1\u20095.3\u2009mm, <jats:italic>P<\/jats:italic>\u2009=\u20090.31). Using combined omics, demographic and clinical data, deep learning displayed good to excellent performance, with an AUC (95% CI) of 0.890 (0.810\u20130.970) for delivery &lt;\u200934 weeks' gestation, 0.890 (0.790\u20130.990) for delivery &lt;\u200928 days post\u2010amniocentesis and 0.792 (0.689\u20130.894) for NICU admission. These values were higher overall than for the other five machine\u2010learning methods, although each individual machine\u2010learning technique yielded statistically significant prediction of the different perinatal outcomes.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>This is the first study to report use of AI with AF proteomics and metabolomics and ultrasound assessment in pregnancy. Machine learning, particularly deep learning, achieved good to excellent prediction of perinatal outcome in asymptomatic pregnant women with short CL in the second trimester. Copyright \u00a9 2018 ISUOG. Published by John Wiley &amp; Sons Ltd.<\/jats:p><\/jats:sec>","DOI":"10.1002\/uog.20168","type":"journal-article","created":{"date-parts":[[2018,11,1]],"date-time":"2018-11-01T05:39:30Z","timestamp":1541050770000},"page":"110-118","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":73,"title":["Artificial intelligence and amniotic fluid multiomics: prediction of perinatal outcome in asymptomatic women with short cervix"],"prefix":"10.1002","volume":"54","author":[{"given":"R. O.","family":"Bahado\u2010Singh","sequence":"first","affiliation":[{"name":"Department of Obstetrics and Gynecology Beaumont Research Institute  Royal Oak MI USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J.","family":"Sonek","sequence":"additional","affiliation":[{"name":"Division of Maternal Fetal Medicine Wright State University  Dayton OH USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"D.","family":"McKenna","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology Miami Valley Hospital South  Tampa FL USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"D.","family":"Cool","sequence":"additional","affiliation":[{"name":"Department of Pharmacology and Toxicology Wright State University  Dayton OH USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"B.","family":"Aydas","sequence":"additional","affiliation":[{"name":"Department of Computer Science Albion College  Albion MI USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"O.","family":"Turkoglu","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology Beaumont Research Institute  Royal Oak MI USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"T.","family":"Bjorndahl","sequence":"additional","affiliation":[{"name":"Department of Biological Science University of Alberta  Edmonton AB Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"R.","family":"Mandal","sequence":"additional","affiliation":[{"name":"Department of Biological Science University of Alberta  Edmonton AB Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"D.","family":"Wishart","sequence":"additional","affiliation":[{"name":"Department of Biological Science University of Alberta  Edmonton AB Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"P.","family":"Friedman","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology Beaumont Research Institute  Royal Oak MI USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S. F.","family":"Graham","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology Beaumont Research Institute  Royal Oak MI USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"A.","family":"Yilmaz","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology Beaumont Research Institute  Royal Oak MI USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2019,7,9]]},"reference":[{"key":"e_1_2_5_2_1","doi-asserted-by":"publisher","DOI":"10.7863\/ultra.15.08035"},{"key":"e_1_2_5_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajog.2011.08.002"},{"key":"e_1_2_5_4_1","unstructured":"BiegelB KuroseJF.The National Artificial Intelligence Research and Development Strategic Plan. White House October 2016."},{"key":"e_1_2_5_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajog.2012.10.237"},{"key":"e_1_2_5_6_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJM199602293340904"},{"key":"e_1_2_5_7_1","doi-asserted-by":"publisher","DOI":"10.1046\/j.1469-0705.2001.00437.x"},{"key":"e_1_2_5_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/uog.1871"},{"key":"e_1_2_5_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/0002-9378(90)91106-M"},{"key":"e_1_2_5_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/0002-9378(93)90009-8"},{"key":"e_1_2_5_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajog.2014.03.056"},{"key":"e_1_2_5_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.mehy.2013.04.043"},{"key":"e_1_2_5_13_1","doi-asserted-by":"publisher","DOI":"10.1177\/1074248412460124"},{"key":"e_1_2_5_14_1","doi-asserted-by":"publisher","DOI":"10.4103\/2348-2907.204672"},{"key":"e_1_2_5_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-60327-194-3_14"},{"key":"e_1_2_5_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11306-012-0482-9"},{"key":"e_1_2_5_17_1","doi-asserted-by":"publisher","DOI":"10.1021\/ac300698c"},{"key":"e_1_2_5_18_1","volume-title":"Data Mining: Practical Machine Learning Tools and Techniques","author":"Witten IH","year":"2011"},{"key":"e_1_2_5_19_1","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jproteome.7b00595"},{"key":"e_1_2_5_20_1","doi-asserted-by":"crossref","unstructured":"KuhnM.Building predictive models in R using the caret package.J Stat Softw2008;28:26.","DOI":"10.18637\/jss.v028.i05"},{"key":"e_1_2_5_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajog.2007.11.026"},{"key":"e_1_2_5_22_1","first-page":"851","article-title":"Deep learning in bioinformatics","volume":"18","author":"Min S","year":"2017","journal-title":"Brief Bioinform"},{"key":"e_1_2_5_23_1","doi-asserted-by":"publisher","DOI":"10.15252\/msb.20156651"},{"key":"e_1_2_5_24_1","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkl923"},{"key":"e_1_2_5_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/0960-0760(94)00175-L"},{"key":"e_1_2_5_26_1","doi-asserted-by":"publisher","DOI":"10.3109\/14767058.2010.482618"},{"key":"e_1_2_5_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajog.2015.03.052"},{"key":"e_1_2_5_28_1","doi-asserted-by":"publisher","DOI":"10.1515\/jpm-2016-0348"},{"key":"e_1_2_5_29_1","doi-asserted-by":"publisher","DOI":"10.1021\/pr4002355"},{"key":"e_1_2_5_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajog.2016.01.181"},{"key":"e_1_2_5_31_1","doi-asserted-by":"publisher","DOI":"10.1515\/JPM.2006.002"},{"key":"e_1_2_5_32_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajog.2010.05.040"}],"container-title":["Ultrasound in Obstetrics &amp; Gynecology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.wiley.com\/onlinelibrary\/tdm\/v1\/articles\/10.1002%2Fuog.20168","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/uog.20168","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/uog.20168","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/obgyn.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/uog.20168","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,10]],"date-time":"2023-09-10T01:34:32Z","timestamp":1694309672000},"score":1,"resource":{"primary":{"URL":"https:\/\/obgyn.onlinelibrary.wiley.com\/doi\/10.1002\/uog.20168"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7]]},"references-count":31,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2019,7]]}},"alternative-id":["10.1002\/uog.20168"],"URL":"https:\/\/doi.org\/10.1002\/uog.20168","archive":["Portico"],"relation":{},"ISSN":["0960-7692","1469-0705"],"issn-type":[{"value":"0960-7692","type":"print"},{"value":"1469-0705","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,7]]},"assertion":[{"value":"2018-07-20","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2018-09-07","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2019-07-09","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}