Machine learning detects intraventricular haemorrhage in extremely preterm infants

Simple item page
dc.contributor.authorAshoori, Minooen
dc.contributor.authorO’Toole, John M.en
dc.contributor.authorO’Halloran, Ken D.en
dc.contributor.authorNaulaers, Gunnaren
dc.contributor.authorThewissen, Liesbethen
dc.contributor.authorMiletin, Janen
dc.contributor.authorCheung, Po-Yinen
dc.contributor.authorEL-Khuffash, Afifen
dc.contributor.authorVan Laere, Daviden
dc.contributor.authorStraňák, Zbyněken
dc.contributor.authorDempsey, Eugene M.en
dc.contributor.authorMcDonald, Fiona B.en
dc.contributor.funderScience Foundation Irelanden
dc.contributor.funderSeventh Framework Programmeen
dc.contributor.funderUniversity College Corken
dc.date.accessioned2023-09-01T09:45:33Z
dc.date.available2023-09-01T09:45:33Z
dc.date.issued2023-05-23en
dc.description.abstractObjective: To test the potential utility of applying machine learning methods to regional cerebral (rcSO2) and peripheral oxygen saturation (SpO2) signals to detect brain injury in extremely preterm infants. Study design: A subset of infants enrolled in the Management of Hypotension in Preterm infants (HIP) trial were analysed (n = 46). All eligible infants were <28 weeks’ gestational age and had continuous rcSO2 measurements performed over the first 72 h and cranial ultrasounds performed during the first week after birth. SpO2 data were available for 32 infants. The rcSO2 and SpO2 signals were preprocessed, and prolonged relative desaturations (PRDs; data-driven desaturation in the 2-to-15-min range) were extracted. Numerous quantitative features were extracted from the biosignals before and after the exclusion of the PRDs within the signals. PRDs were also evaluated as a stand-alone feature. A machine learning model was used to detect brain injury (intraventricular haemorrhage-IVH grade II–IV) using a leave-one-out cross-validation approach. Results: The area under the receiver operating characteristic curve (AUC) for the PRD rcSO2 was 0.846 (95% CI: 0.720–0.948), outperforming the rcSO2 threshold approach (AUC 0.593 95% CI 0.399–0.775). Neither the clinical model nor any of the SpO2 models were significantly associated with brain injury. Conclusion: There was a significant association between the data-driven definition of PRDs in rcSO2 and brain injury. Automated analysis of PRDs of the cerebral NIRS signal in extremely preterm infants may aid in better prediction of IVH compared with a threshold-based approach. Further investigation of the definition of the extracted PRDs and an understanding of the physiology underlying these events are required.en
dc.description.sponsorshipUniversity College Cork (Department of Physiology)en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.articleid917en
dc.identifier.citationAshoori, M., O’Toole, J.M., O’Halloran, K.D., Naulaers, G., Thewissen, L., Miletin, J., Cheung, P.-Y., EL-Khuffash, A., Van Laere, D., Straňák, Z., Dempsey, E.M. and McDonald, F.B. (2023) ‘Machine learning detects intraventricular haemorrhage in extremely preterm infants’, Children, 10(6), 917 (13 pp). https://doi.org/10.3390/children10060917.en
dc.identifier.doi10.3390/children10060917en
dc.identifier.endpage13en
dc.identifier.issn2227-9067en
dc.identifier.issued6en
dc.identifier.journaltitleChildrenen
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/14897
dc.identifier.volume10en
dc.language.isoenen
dc.publisherMDPIen
dc.relation.ispartofChildrenen
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Starting Investigator Research Grant/18/SIRG/5483/IE/A trilogy of stressors in the NICU: Towards therapy for preterm adversity./en
dc.relation.projectinfo:eu-repo/grantAgreement/EC/FP7::SP1::HEALTH/260777/EU/Management of Hypotension In the Preterm Extremely Low Gestational Age Newborn/THE HIP TRIALen
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2272/IE/Irish Centre for Fetal and Neonatal Translational Research (INFANT)/en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Starting Investigator Research Grant/15/SIRG/3580/IE/Advancing Neuroprotection for Premature Infants: Automated Analysis of Neurological Signals for Early Detection of Brain Injury/en
dc.rights© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectNear-infrared spectroscopy (NIRS)en
dc.subjectRegional cerebral oxygen saturation (rcSO2)en
dc.subjectPeripheral oxygen saturation (SpO2)en
dc.subjectProlonged relative desaturation (PRD)en
dc.subjectExtreme gradient boosting (XGBoost)en
dc.titleMachine learning detects intraventricular haemorrhage in extremely preterm infantsen
dc.typeArticle (peer-reviewed)en
oaire.citation.issue6en
oaire.citation.volume10en
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
children-10-00917-v2.pdf
Size:
1.66 MB
Format:
Adobe Portable Document Format
Description:
Published version
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
2.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Physiology - Journal Articles
INFANT Research Centre - Journal Articles
Medicine - Journal Articles