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Classification of brain injury severity using a hybrid broadband NIRS and DCS instrument with a machine learning approach

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. Green open access

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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.

Type: Proceedings paper
Title: Classification of brain injury severity using a hybrid broadband NIRS and DCS instrument with a machine learning approach
Event: European Conferences on Biomedical Optics 2023
ISBN-13: 9781510664654
Open access status: An open access version is available from UCL Discovery
DOI: 10.1117/12.2670657
Publisher version: https://doi.org/10.1117/12.2670657
Language: English
Additional information: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL EGA Institute for Womens Health
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL EGA Institute for Womens Health > Neonatology
URI: https://discovery.ucl.ac.uk/id/eprint/10179358
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