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The study also examined whether quantitative rcSO\n                      <jats:sub>2<\/jats:sub>\n                      features are associated with grade of hypoxic ischaemic encephalopathy (HIE). We analysed 58 term infants with HIE (&gt;36\u00a0weeks of gestational age) enrolled in a prospective observational study. All newborn infants had a period of continuous rcSO\n                      <jats:sub>2<\/jats:sub>\n                      \u00a0monitoring and magnetic resonance imaging (MRI) assessment during the first week of life. rcSO\n                      <jats:sub>2\u00a0<\/jats:sub>\n                      Signals were pre\u2010processed and quantitative features were extracted. Machine\u2010learning and deep\u2010learning models were developed to detect adverse outcome (brain injury on MRI or death in the first week) using the leave\u2010one\u2010out cross\u2010validation approach and to assess the association between rcSO\n                      <jats:sub>2<\/jats:sub>\n                      and HIE grade (modified Sarnat\u00a0\u2013\u00a0at 1\u00a0h). The machine\u2010learning model (rcSO\n                      <jats:sub>2<\/jats:sub>\n                      excluding prolonged relative desaturations) significantly detected infant MRI outcome or death in the first week of life [area under the curve (AUC)\u00a0=\u00a00.73, confidence interval (CI)\u00a0=\u00a00.59\u22120.86, Matthew's correlation coefficient\u00a0=\u00a00.35]. In agreement, deep learning models detected adverse outcome with an AUC\u00a0=\u00a00.64, CI\u00a0=\u00a00.50\u22120.79. We also report a significant association between rcSO\n                      <jats:sub>2<\/jats:sub>\n                      features and HIE grade using a machine learning approach (AUC\u00a0=\u00a00.81, CI\u00a0=\u00a00.73\u22120.90). We conclude that automated analysis of rcSO\n                      <jats:sub>2<\/jats:sub>\n                      using machine learning methods in term infants with HIE was able to determine, with modest accuracy, infants with adverse outcome.\n                      <jats:italic>De novo<\/jats:italic>\n                      approaches to signal analysis of NIRS holds promise to aid clinical decision making in the future.\n                      <jats:boxed-text content-type=\"graphic\" position=\"anchor\">\n                        <jats:graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" mimetype=\"image\/png\" position=\"anchor\" specific-use=\"enlarged-web-image\" xlink:href=\"graphic\/tjp16378-gra-0001-m.png\">\n                          <jats:alt-text>image<\/jats:alt-text>\n                        <\/jats:graphic>\n                      <\/jats:boxed-text>\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Key points<\/jats:title>\n                    <jats:p>\n                      <jats:list list-type=\"bullet\">\n                        <jats:list-item>\n                          <jats:p>Hypoxic\u2010induced neonatal brain injury contributes to both short\u2010 and long\u2010term functional deficits.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>Non\u2010invasive continuous monitoring of brain oxygenation using near\u2010infrared\u2010 spectroscopy offers a potential new insight to the development of serious injury.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>In this study, characteristics of the NIRS signal were summarised using either predefined features or data\u2010driven feature extraction, both were combined with a machine learning approach to predict short\u2010term brain injury.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>\n                            Using data from a cohort of term infants with hypoxic ischaemic encephalopathy, the present study illustrates that automated analysis of regional cerebral oxygen saturation rcSO\n                            <jats:sub>2<\/jats:sub>\n                            , using either machine learning or deep learning methods, was able to determine infants with adverse outcome.\n                          <\/jats:p>\n                        <\/jats:list-item>\n                      <\/jats:list>\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1113\/jp287001","type":"journal-article","created":{"date-parts":[[2024,10,19]],"date-time":"2024-10-19T10:59:46Z","timestamp":1729335586000},"page":"6347-6360","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Machine learning models of cerebral oxygenation (rcSO\n                    <sub>2<\/sub>\n                    ) for brain injury detection in neonates with hypoxic\u2010ischaemic encephalopathy"],"prefix":"10.1113","volume":"602","author":[{"given":"Minoo","family":"Ashoori","sequence":"first","affiliation":[{"name":"INFANT Research Centre University College Cork  Cork Ireland"},{"name":"Department of Physiology, School of Medicine, College of Medicine and Health University College Cork  Cork Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John M.","family":"O'Toole","sequence":"additional","affiliation":[{"name":"INFANT Research Centre University College Cork  Cork Ireland"},{"name":"Department of Paediatrics and Child Health University College Cork  Cork Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aisling A.","family":"Garvey","sequence":"additional","affiliation":[{"name":"INFANT Research Centre University College Cork  Cork 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