{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Stevenson NJ"],"funding":["Austrian Science Fund FWF","Suomalainen Tiedeakatemia","European Commission"],"pagination":["1564-1573"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC7480927"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["7(9)"],"pubmed_abstract":["<h4>Objectives</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.<h4>Methods</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.<h4>Results</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"],"journal":["Annals of clinical and translational neurology"],"pubmed_title":["Reliability and accuracy of EEG interpretation for estimating age in preterm infants."],"pmcid":["PMC7480927"],"funding_grant_id":["KLI237","3104450","H2020‐MCSA‐IF‐656131","313242","288220"],"pubmed_authors":["Clancy RR","Kaminska A","Tataranno ML","Stevenson NJ","Griesmaier E","Roberts JA","Klebermass-Schrehof K","Pavlidis E","Vanhatalo S"],"additional_accession":[]},"is_claimable":false,"name":"Reliability and accuracy of EEG interpretation for estimating age in preterm infants.","description":"<h4>Objectives</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.<h4>Methods</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.<h4>Results</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","dates":{"release":"2020-01-01T00:00:00Z","publication":"2020 Sep","modification":"2025-06-01T00:14:45.463Z","creation":"2025-06-01T00:14:45.463Z"},"accession":"S-EPMC7480927","cross_references":{"pubmed":["32767645"],"doi":["10.1002/acn3.51132"]}}