TY - GEN AV - public SN - 0277-786X A1 - Bili, Danai A1 - Lange, Frédéric A1 - Jones, Kelly H A1 - Parfentyeva, Veronika A1 - Durduran, Turgut A1 - Robertson, Nikki A1 - Mitra, Subhabrata A1 - Tachtsidis, Ilias N2 - Optical biomarkers of neonatal hypoxic ischemic (HI) brain injury can offer the advantage of continuous, cot-side assessment of the degree of injury; research thus far has focused on examining different optical measured brain physiological signals and feature combinations to achieve this. To maximize the breadth of physiological characteristics being taken into consideration, a multimodal optical platform has been developed, allowing unique physiological insights into brain injury. In this paper we present an assessment of severity of injury using a state-of-the-art hybrid broadband Near Infrared Spectrometer (bNIRS) and Diffusion Correlation Spectrometer (DCS) instrument called FLORENCE with a machine learning pipeline. We demonstrate in the preclinical neonatal model (the newborn piglet) that our approach can identify different HI insult severity (controls, mild, severe). We show that a machine learning pipeline based on k-means clustering can be used to differentiate between the controls and the HI piglets with an accuracy of 78%, the mild severity insult piglets from the severe insult piglets with an accuracy of 90% and can also differentiate the 3 piglet groups with an accuracy of 80%. So, this analytics pipeline demonstrates how optical data from multiple instruments can be processed towards markers of brain health. T3 - European Conferences on Biomedical Optics PB - SPIE ID - discovery10179358 UR - https://doi.org/10.1117/12.2670657 Y1 - 2023/08/09/ TI - Classification of brain injury severity using a hybrid broadband NIRS and DCS instrument with a machine learning approach CY - Munich, Germany N1 - This version is the version of record. For information on re-use, please refer to the publisher?s terms and conditions. ER -