<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Stevenson NJ</submitter><funding>Austrian Science Fund FWF</funding><funding>Suomalainen Tiedeakatemia</funding><funding>European Commission</funding><pagination>1564-1573</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC7480927</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>7(9)</volume><pubmed_abstract>&lt;h4>Objectives&lt;/h4>To determine the accuracy of, and agreement among, EEG and aEEG readers' estimation of maturity and a novel computational measure of functional brain age (FBA) in preterm infants.&lt;h4>Methods&lt;/h4>Seven experts estimated the postmenstrual ages (PMA) in a cohort of recordings from preterm infants using cloud-based review software. The FBA was calculated using a machine learning-based algorithm. Error analysis was used to determine the accuracy of PMA assessments and intraclass correlation (ICC) was used to assess agreement between experts.&lt;h4>Results&lt;/h4>EEG recordings from a PMA range 25 to 38 weeks were successfully interpreted. In 179 recordings from 62 infants interpreted by all human readers, there was moderate agreement between experts (aEEG ICC = 0.724; 95%CI:0.658-0</pubmed_abstract><journal>Annals of clinical and translational neurology</journal><pubmed_title>Reliability and accuracy of EEG interpretation for estimating age in preterm infants.</pubmed_title><pmcid>PMC7480927</pmcid><funding_grant_id>KLI237</funding_grant_id><funding_grant_id>3104450</funding_grant_id><funding_grant_id>H2020‐MCSA‐IF‐656131</funding_grant_id><funding_grant_id>313242</funding_grant_id><funding_grant_id>288220</funding_grant_id><pubmed_authors>Clancy RR</pubmed_authors><pubmed_authors>Kaminska A</pubmed_authors><pubmed_authors>Tataranno ML</pubmed_authors><pubmed_authors>Stevenson NJ</pubmed_authors><pubmed_authors>Griesmaier E</pubmed_authors><pubmed_authors>Roberts JA</pubmed_authors><pubmed_authors>Klebermass-Schrehof K</pubmed_authors><pubmed_authors>Pavlidis E</pubmed_authors><pubmed_authors>Vanhatalo S</pubmed_authors></additional><is_claimable>false</is_claimable><name>Reliability and accuracy of EEG interpretation for estimating age in preterm infants.</name><description>&lt;h4>Objectives&lt;/h4>To determine the accuracy of, and agreement among, EEG and aEEG readers' estimation of maturity and a novel computational measure of functional brain age (FBA) in preterm infants.&lt;h4>Methods&lt;/h4>Seven experts estimated the postmenstrual ages (PMA) in a cohort of recordings from preterm infants using cloud-based review software. The FBA was calculated using a machine learning-based algorithm. Error analysis was used to determine the accuracy of PMA assessments and intraclass correlation (ICC) was used to assess agreement between experts.&lt;h4>Results&lt;/h4>EEG recordings from a PMA range 25 to 38 weeks were successfully interpreted. In 179 recordings from 62 infants interpreted by all human readers, there was moderate agreement between experts (aEEG ICC = 0.724; 95%CI:0.658-0</description><dates><release>2020-01-01T00:00:00Z</release><publication>2020 Sep</publication><modification>2025-06-01T00:14:45.463Z</modification><creation>2025-06-01T00:14:45.463Z</creation></dates><accession>S-EPMC7480927</accession><cross_references><pubmed>32767645</pubmed><doi>10.1002/acn3.51132</doi></cross_references></HashMap>