Orbit image analysis machine learning software can be used for the histological quantification of acute ischemic stroke blood clots
Murphree Jr, Dennis H.
Gounis, Matthew J.
Kallmes, David F.
Doyle, Karen M.
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Seán Fitzgerald, Daying Dai, Shunli Wang, Dennis H. Murphree Jr., Abhay Pandit, Andrew Douglas, Asim Rizvi, Ramanathan Kadirvel, Michael Gilvarry, Ray McCarthy, Manuel Stritt, Matthew J. Gounis, Waleed Brinjikji, David F. Kallmes, Karen M. Doyle (2019) . Orbit image analysis machine learning software can be used for the histological quantification of acute ischemic stroke blood clots. PLoS ONE 14(12): e0225841. https://doi.org/10.1371/journal.pone.0225841
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 < 0.001, n = 150). A significant relationship was found between clot composition (RBC-Rich, Mixed, Fibrin-Rich) and the presence of a Hyperdense artery sign using the algorithmic method (X2(2) = 6.712, p = 0.035*) but not using the reference standard method (X2(2) = 3.924, p = 0.141). Orbit Image Analysis machine learning software can be used for the histological quantification of AIS clots, reproducibly generating composition analyses similar to current reference standard methods.
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