UMMS Affiliation

Department of Radiology, New England Center for Stroke Research

Publication Date


Document Type



Artificial Intelligence and Robotics | Cardiovascular Diseases | Nervous System Diseases | Pathological Conditions, Signs and Symptoms | Pathology | Radiology


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 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.


Image analysis, Histology, Fibrin, Computed axial tomography, Red blood cells, Hematoxylin staining, Machine learning, Machine learning algorithms

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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.

DOI of Published Version



PLoS One. 2019 Dec 5;14(12):e0225841. doi: 10.1371/journal.pone.0225841. eCollection 2019. Link to article on publisher's site

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PloS one

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Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.