Machine Learning Detects Intraventricular Haemorrhage in Extremely Preterm Infants.

ABSTRACT:

Objective

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) wer

SUBMITTER: Ashoori M 

PROVIDER: S-EPMC10297241 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature