Orbit image analysis machine learning software can be used for the histological quantification of acute ischemic stroke blood clots

Seán Fitzgerald, Shunli Wang, Daying Dai, Dennis H Murphree, Abhay Pandit, Andrew Douglas, Asim Rizvi, Ramanathan Kadirvel, Michael Gilvarry, Ray McCarthy, Manuel Stritt, Matthew J Gounis, Waleed Brinjikji, David F Kallmes and Karen M Doyle

PLOS ONE, 2019, vol. 14, issue 12, 1-14

Abstract: Our aim was to assess the utility of a novel machine learning software (Orbit Image Analysis) in the histological quantification of acute ischemic stroke (AIS) clots. We analyzed 50 AIS blood clots retrieved using mechanical thrombectomy procedures. Following H&E staining, quantification of clot components was performed by two different methods: a pathologist using a reference standard method (Adobe Photoshop CC) and an experienced researcher using Orbit Image Analysis. Following quantification, the clots were categorized into 3 types: RBC dominant (≥60% RBCs), Mixed and Fibrin dominant (≥60% Fibrin). Correlations between clot composition and Hounsfield Units density on Computed Tomography (CT) were assessed. There was a significant correlation between the components of clots as quantified by the Orbit Image Analysis algorithm and the reference standard approach (ρ = 0.944**, p

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0225841

DOI: 10.1371/journal.pone.0225841

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