@inproceedings{discovery10179358, series = {European Conferences on Biomedical Optics}, year = {2023}, volume = {12628}, title = {Classification of brain injury severity using a hybrid broadband NIRS and DCS instrument with a machine learning approach}, editor = {Davide Contini and Yoko Hoshi and Thomas D O'Sullivan}, month = {August}, address = {Munich, Germany}, booktitle = {Proceedings Volume 12628, Diffuse Optical Spectroscopy and Imaging IX}, pages = {126280D}, journal = {Proceedings of SPIE - The International Society for Optical Engineering}, note = {This version is the version of record. For information on re-use, please refer to the publisher's terms and conditions.}, publisher = {SPIE}, author = {Bili, Danai and Lange, Fr{\'e}d{\'e}ric and Jones, Kelly H and Parfentyeva, Veronika and Durduran, Turgut and Robertson, Nikki and Mitra, Subhabrata and Tachtsidis, Ilias}, url = {https://doi.org/10.1117/12.2670657}, abstract = {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.}, issn = {0277-786X} }