Machine Learning Detects Intraventricular Haemorrhage in Extremely Preterm Infants

Bibliographic Data

ID15716252
AuthorsMinoo Ashoori (0000-0001-8392-7896, University College Cork), John M O’Toole (0000-0001-9021-1183, University College Cork), Ken D O’Halloran (0000-0002-8832-1153, University College Cork), Gunnar Naulaers (0000-0003-0735-4808, KU Leuven), Liesbeth Thewissen (0000-0002-0991-3776, KU Leuven), Jan Miletín (0000-0003-0489-6054, Coombe Women & Infants University Hospital), Po‐Yin Cheung (0000-0002-7141-1523, University of Alberta), Afif El‐Khuffash (0000-0002-2933-822X, Royal College of Surgeons in Ireland), David Van Laere (Neonatale Intensive Care Unit, Universitair Ziekenhuis, (UZ) Antwerp, Drie Eikenstraat 655, 2650 Antwerp, Belgium), Zbyněk Straňák (0000-0001-7095-3198, Charles University), Eugene Dempsey (0000-0002-6266-3462, University College Cork), Eugene M Dempsey (INFANT Research Centre, University College Cork, T12 AK54 Cork, Ireland), Fiona B McDonald (0000-0002-8644-1004, University College Cork, corresponding author)
Year2023
Volume10
Issue6
Pages917-917
Publication date2023-05-23
Open AccessYes
TypeARTICLE
VenueChildren (JOURNAL)
Journal identifiersISSN: 2227-9067 • E-ISSN: 2227-9067
PublisherMultidisciplinary Digital Publishing Institute (PUBLISHER • CH)
DOI10.3390/children10060917
PMID37371150
OpenAlexW4377987141
LanguageEN
Citations received1
References cited44

There was a significant association between the data-driven definition of PRDs in rcSO 2 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

Anesthesia · Gestational age · Intraventricular hemorrhage · Pregnancy · Receiver operating characteristic · Medicine · Neonatal and fetal brain pathology · Neonatal Respiratory Health Research · Non-Invasive Vital Sign Monitoring · Internal Medicine

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