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

Author

Listed:
  • Seán Fitzgerald
  • Shunli Wang
  • Daying Dai
  • Dennis H Murphree Jr.
  • 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

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

Suggested Citation

  • Seán Fitzgerald & Shunli Wang & Daying Dai & Dennis H Murphree Jr. & Abhay Pandit & Andrew Douglas & Asim Rizvi & Ramanathan Kadirvel & Michael Gilvarry & Ray McCarthy & Manuel Stritt & Matthew J Goun, 2019. "Orbit image analysis machine learning software can be used for the histological quantification of acute ischemic stroke blood clots," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-14, December.
    • Handle: RePEc:plo:pone00:0225841
    DOI: 10.1371/journal.pone.0225841
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