Peer-Reviewed Journal Details
Mandatory Fields
Fitzgerald, S;Wang, SL;Dai, DY;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
December
Plos One
Orbit image analysis machine learning software can be used for the histological quantification of acute ischemic stroke blood clots
Published
Optional Fields
STENT-RETRIEVER THROMBECTOMY THROMBUS ASPIRIN DENSITY ATTACK RISK
14
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 H&E 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 (X-2 (2) = 6.712, p = 0.035*) but not using the reference standard method (X-2 (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.
1932-6203
10.1371/journal.pone.0225841
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