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dc.contributor.authorFitzgerald, Sean
dc.contributor.authorWang, Shunli
dc.contributor.authorDai, Daying
dc.contributor.authorMurphree, Dennis H. Jr.
dc.contributor.authorPandit, Abhay
dc.contributor.authorDouglas, Andrew
dc.contributor.authorRizvi, Asim
dc.contributor.authorKadirvel, Ramanathan
dc.contributor.authorGilvarry, Michael
dc.contributor.authorMcCarthy, Ray
dc.contributor.authorStritt, Manuel
dc.contributor.authorGounis, Matthew J.
dc.contributor.authorBrinjikji, Waleed
dc.contributor.authorKallmes, David F.
dc.contributor.authorDoyle, Karen M.
dc.date2022-08-11T08:09:55.000
dc.date.accessioned2022-08-23T16:48:42Z
dc.date.available2022-08-23T16:48:42Z
dc.date.issued2019-12-05
dc.date.submitted2020-01-21
dc.identifier.citation<p>PLoS One. 2019 Dec 5;14(12):e0225841. doi: 10.1371/journal.pone.0225841. eCollection 2019. <a href="https://doi.org/10.1371/journal.pone.0225841">Link to article on publisher's site</a></p>
dc.identifier.issn1932-6203 (Linking)
dc.identifier.doi10.1371/journal.pone.0225841
dc.identifier.pmid31805096
dc.identifier.urihttp://hdl.handle.net/20.500.14038/41319
dc.description.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.
dc.language.isoen_US
dc.relation<p><a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Retrieve&list_uids=31805096&dopt=Abstract">Link to Article in PubMed</a></p>
dc.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.
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectImage analysis
dc.subjectHistology
dc.subjectFibrin
dc.subjectComputed axial tomography
dc.subjectRed blood cells
dc.subjectHematoxylin staining
dc.subjectMachine learning
dc.subjectMachine learning algorithms
dc.subjectArtificial Intelligence and Robotics
dc.subjectCardiovascular Diseases
dc.subjectNervous System Diseases
dc.subjectPathological Conditions, Signs and Symptoms
dc.subjectPathology
dc.subjectRadiology
dc.titleOrbit image analysis machine learning software can be used for the histological quantification of acute ischemic stroke blood clots
dc.typeJournal Article
dc.source.journaltitlePloS one
dc.source.volume14
dc.source.issue12
dc.identifier.legacyfulltexthttps://escholarship.umassmed.edu/cgi/viewcontent.cgi?article=5123&amp;context=oapubs&amp;unstamped=1
dc.identifier.legacycoverpagehttps://escholarship.umassmed.edu/oapubs/4104
dc.identifier.contextkey16292494
refterms.dateFOA2022-08-23T16:48:42Z
html.description.abstract<p>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.</p>
dc.identifier.submissionpathoapubs/4104
dc.contributor.departmentDepartment of Radiology, New England Center for Stroke Research
dc.source.pagese0225841


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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.
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.