Orbit image analysis machine learning software can be used for the histological quantification of acute ischemic stroke blood clots
Murphree, Dennis H. Jr.
Gounis, Matthew J.
Kallmes, David F.
Doyle, Karen M.
UMass Chan AffiliationsDepartment of Radiology, New England Center for Stroke Research
Computed axial tomography
Red blood cells
Machine learning algorithms
Artificial Intelligence and Robotics
Nervous System Diseases
Pathological Conditions, Signs and Symptoms
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AbstractOur 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 HandE 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 (rho = 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.
PLoS One. 2019 Dec 5;14(12):e0225841. doi: 10.1371/journal.pone.0225841. eCollection 2019. Link to article on publisher's site
Permanent Link to this Itemhttp://hdl.handle.net/20.500.14038/41319
RightsCopyright: © 2019 Fitzgerald et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Except where otherwise noted, this item's license is described as Copyright: © 2019 Fitzgerald et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.