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

• Published in: PloS one 2019-12, Vol.14 (12), p.e0225841-e0225841
• Main Authors: Fitzgerald, Seán, Wang, Shunli, Dai, Daying, Murphree, Jr, Dennis H, Pandit, Abhay, Douglas, Andrew, Rizvi, Asim, Kadirvel, Ramanathan, Gilvarry, Michael, McCarthy, Ray, Stritt, Manuel, Gounis, Matthew J, Brinjikji, Waleed, Kallmes, David F, Doyle, Karen M
• Format: Article
• Language: English
• Subjects: Adult; Aged; Aged, 80 and over; Algorithms; Artificial intelligence; Biology and Life Sciences; Blood; Blood clots; Blood coagulation; Brain Ischemia - complications; Brain Ischemia - diagnostic imaging; Brain Ischemia - pathology; Cohort Studies; Composition; Computed tomography; Computer and Information Sciences; Computer programs; Correlation; Digital imaging; Erythrocytes - pathology; Female; Fibrin; Health sciences; Histology; Humans; Image analysis; Image processing; Image Processing, Computer-Assisted; Ischemia; Learning algorithms; Machine Learning; Male; Medical equipment; Medical imaging; Medicine and Health Sciences; Methods; Middle Aged; Orbit - diagnostic imaging; Orbit - pathology; Pathology; Patients; Physical Sciences; Physiology; Research and Analysis Methods; Software; Stroke; Stroke - complications; Stroke - diagnostic imaging; Stroke - pathology; Surgery; Thrombosis - complications; Thrombosis - diagnostic imaging; Thrombosis - pathology; Young Adult
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0225841

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.