Department of Radiology, New England Center for Stroke Research
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
Fitzgerald S, Wang S, Dai D, Murphree DH, Pandit A, Douglas A, Rizvi A, Kadirvel R, Gilvarry M, McCarthy R, Stritt M, Gounis MJ, Brinjikji W, Kallmes DF, Doyle KM. (2019). Orbit image analysis machine learning software can be used for the histological quantification of acute ischemic stroke blood clots. Open Access Articles. https://doi.org/10.1371/journal.pone.0225841. Retrieved from https://escholarship.umassmed.edu/oapubs/4104
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This work is licensed under a Creative Commons Attribution 4.0 License.
Artificial Intelligence and Robotics Commons, Cardiovascular Diseases Commons, Nervous System Diseases Commons, Pathological Conditions, Signs and Symptoms Commons, Pathology Commons, Radiology Commons