Bili, Danai;
Lange, Frédéric;
Jones, Kelly H;
Parfentyeva, Veronika;
Durduran, Turgut;
Robertson, Nikki;
Mitra, Subhabrata;
(2023)
Classification of brain injury severity using a hybrid broadband NIRS and DCS instrument with a machine learning approach.
In: Contini, Davide and Hoshi, Yoko and O'Sullivan, Thomas D, (eds.)
Proceedings Volume 12628, Diffuse Optical Spectroscopy and Imaging IX.
(pp. 126280D).
SPIE: Munich, Germany.
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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.
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