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

Preterm 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.

Grantor dc:publisher

University College Cork

Year dc:date.issued

2024

Author dc:creator

• Ashoori, Minoo

Advisors dc:contributor.advisor

• McDonald, Fiona
• O'Halloran, Ken

• Near-infrared spectroscopy (NIRS)
• Regional cerebral oxygen saturation (rcSO2)
• Peripheral oxygen saturation (SpO2)
• Prolonged relative desaturation (PRD)
• Extreme gradient boosting (XGBoost)
• Convolutional neural network (CNN)
• Magnetic resonance imaging (MRI)
• Hypoxic-ischaemic encephalopathy (HIE)
• Intraventricular haemorrhage (IVH)
• Machine learning
• Deep learning
• Neonatal brain injury
• Oxygen delivery

Statement dc:rights

• © 2024, Minoo Ashoori.

Licence dc:rights.uri

• https://creativecommons.org/licenses/by-nc-nd/4.0/

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10468/17059

OAI identifier oai:identifier

oai:cora.ucc.ie:10468/17059