UMMS Affiliation
Department of Radiology, New England Center for Stroke Research
Publication Date
2019-12-05
Document Type
Article
Disciplines
Artificial Intelligence and Robotics | Cardiovascular Diseases | Nervous System Diseases | Pathological Conditions, Signs and Symptoms | Pathology | Radiology
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 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.
Keywords
Image analysis, Histology, Fibrin, Computed axial tomography, Red blood cells, Hematoxylin staining, Machine learning, Machine learning algorithms
Rights and Permissions
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
10.1371/journal.pone.0225841
Source
PLoS One. 2019 Dec 5;14(12):e0225841. doi: 10.1371/journal.pone.0225841. eCollection 2019. Link to article on publisher's site
Journal/Book/Conference Title
PloS one
Related Resources
PubMed ID
31805096
Repository Citation
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
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Included in
Artificial Intelligence and Robotics Commons, Cardiovascular Diseases Commons, Nervous System Diseases Commons, Pathological Conditions, Signs and Symptoms Commons, Pathology Commons, Radiology Commons