AI-based analysis of cerebral oxygenation in preterm and term infants

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dc.check.chapterOfThesisChapter 4 for 1 yearen
dc.contributor.advisorMcDonald, Fiona
dc.contributor.advisorO'Halloran, Ken
dc.contributor.authorAshoori, Minooen
dc.contributor.funderScience Foundation Ireland
dc.date.accessioned2025-02-18T11:18:20Z
dc.date.available2025-02-18T11:18:20Z
dc.date.issued2024
dc.date.submitted2024
dc.description.abstractPreterm and term infants are vulnerable to brain injuries resulting from inadequate cerebral oxygenation, posing significant risks to their long-term neurodevelopment. Near-infrared spectroscopy (NIRS) is a non-invasive technology capable of monitoring regional cerebral oxygen saturation (rcSO2) and detecting cerebral desaturation events in neonates. However, the clinical utility of NIRS signals remains limited due to challenges in interpreting complex patterns. This thesis aimed to enhance the diagnostic and prognostic value of NIRS by employing advanced signal processing and artificial intelligence (AI) techniques. We developed and optimized methods to extract prolonged relative desaturations (PRDs) from NIRS signals and combined these with machine learning (ML) and deep learning approaches. These models integrated quantitative features derived from rcSO2 and peripheral oxygen saturation (SpO2) signals to predict brain injuries, such as intraventricular hemorrhage (IVH) in preterm infants and hypoxic-ischemic encephalopathy (HIE) in term infants. Our findings demonstrated that features extracted from PRDs significantly outperformed traditional threshold-based approaches in predicting adverse outcomes, achieving an area under the receiver operating characteristic curve (AUC) of 0.846 for IVH detection in preterm infants. Additionally, machine learning models revealed a significant association between rcSO2 patterns and adverse outcomed assessed by MRI, with AUC values reaching 0.73. Deep learning methods further automated feature extraction, providing modest accuracy in detecting MRI-confirmed brain injuries. These results highlight the potential of integrating NIRS with AI-driven analysis to improve early detection and management of neonatal brain injuries. This research lays the foundation for more personalized and timely interventions, ultimately advancing neonatal care and outcomes.en
dc.description.statusNot peer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationAshoori, M. 2024. AI-based analysis of cerebral oxygenation in preterm and term infants. PhD Thesis, University College Cork.
dc.identifier.endpage321
dc.identifier.urihttps://hdl.handle.net/10468/17059
dc.language.isoenen
dc.publisherUniversity College Corken
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/
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./
dc.rights© 2024, Minoo Ashoori.
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectNear-infrared spectroscopy (NIRS)
dc.subjectRegional cerebral oxygen saturation (rcSO2)
dc.subjectPeripheral oxygen saturation (SpO2)
dc.subjectProlonged relative desaturation (PRD)
dc.subjectExtreme gradient boosting (XGBoost)
dc.subjectConvolutional neural network (CNN)
dc.subjectMagnetic resonance imaging (MRI)
dc.subjectHypoxic-ischaemic encephalopathy (HIE)
dc.subjectIntraventricular haemorrhage (IVH)
dc.subjectMachine learning
dc.subjectDeep learning
dc.subjectNeonatal brain injury
dc.subjectOxygen delivery
dc.titleAI-based analysis of cerebral oxygenation in preterm and term infants
dc.typeDoctoral thesisen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePhD - Doctor of Philosophyen
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