Orbit image analysis machine learning software can be used for the histological quantification of acute ischemic stroke blood clots

Seán Fitzgerald, Shunli Wang, Daying Dai, Dennis H. Murphree, Abhay Pandit, Andrew Douglas, Asim Rizvi, Ramanathan Kadirvel, Michael Gilvarry, Ray McCarthy, Manuel Stritt, Matthew J. Gounis, Waleed Brinjikji, David F. Kallmes, Karen M. Doyle

Research output: Contribution to journalArticle

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 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 (ρ = 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.

Original languageEnglish (US)
Article numbere0225841
JournalPloS one
Volume14
Issue number12
DOIs
StatePublished - Dec 1 2019

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)
  • General

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