Machine learning models of cerebral oxygenation (rcSO₂) for brain injury detection in neonates with hypoxic‐ischaemic encephalopathy

The present study was designed to test the potential utility of regional cerebral oxygen saturation (rcSO₂) in detecting term infants with brain injury. The study also examined whether quantitative rcSO₂ features are associated with grade of hypoxic ischaemic encephalopathy (HIE). We analysed 58 term infants with HIE (>36 weeks of gestational age) enrolled in a prospective observational study. All newborn infants had a period of continuous rcSO₂ monitoring and magnetic resonance imaging (MRI) assessment during the first week of life. rcSO₂Signals were pre-processed and quantitative features were extracted. Machine-learning and deep-learning models were developed to detect adverse outcome (brain injury on MRI or death in the first week) using the leave-one-out cross-validation approach and to assess the association between rcSO₂ and HIE grade (modified Sarnat - at 1 h). The machine-learning model (rcSO₂ excluding prolonged relative desaturations) significantly detected infant MRI outcome or death in the first week of life [area under the curve (AUC) = 0.73, confidence interval (CI) = 0.59-0.86, Matthew's correlation coefficient = 0.35]. In agreement, deep learning models detected adverse outcome with an AUC = 0.64, CI = 0.50-0.79. We also report a significant association between rcSO₂ features and HIE grade using a machine learning approach (AUC = 0.81, CI = 0.73-0.90). We conclude that automated analysis of rcSO₂ using machine learning methods in term infants with HIE was able to determine, with modest accuracy, infants with adverse outcome. De novo approaches to signal analysis of NIRS holds promise to aid clinical decision making in the future. KEY POINTS: Hypoxic-induced neonatal brain injury contributes to both short- and long-term functional deficits. Non-invasive continuous monitoring of brain oxygenation using near-infrared- spectroscopy offers a potential new insight to the development of serious injury. In this study, characteristics of the NIRS signal were summarised using either predefined features or data-driven feature extraction, both were combined with a machine learning approach to predict short-term brain injury. Using data from a cohort of term infants with hypoxic ischaemic encephalopathy, the present study illustrates that automated analysis of regional cerebral oxygen saturation rcSO₂, using either machine learning or deep learning methods, was able to determine infants with adverse outcome.