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

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 (\u3e/=60% RBCs), Mixed and Fibrin dominant ( \u3e /=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 \u3c 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

Analysis and quantification of endovascular coil distribution inside saccular aneurysms using histological images

OBJECTIVE: Endovascular coiling is often performed first by placing coils along the aneurysm wall to create a frame and then by filling up the aneurysm core. However, little attention has been paid to quantify this filling strategy and to see how it changes for different packing densities. The purpose of this work is to analyze and quantify endovascular coil distribution inside aneurysms based on serial histological images of experimental aneurysms. METHOD: Seventeen histological images from ten elastase-induced saccular aneurysms in rabbits treated with coils were studied. In-slice coil density, defined as the area taken up by coil winds, was calculated on each histological image. Images were analyzed by partitioning the aneurysm along its longitudinal and radial axis. Coil distribution was quantified by measuring and comparing the in-slice coil density of each partition. RESULTS: Mean total in-slice coil density was 22.0% ± 6.2% (range 10.1% to 30.2%). The density was non-significantly different (p=0.465) along the longitudinal axis. A significant difference (p<0.001) between peripheral and core densities was found. Additionally, peripheral-core density ratio was observed to be inversely proportional to the total in-slice coil density (R(2)=0.57, p<0.001). This ratio was near unity for high in-slice coil density (around 30%). CONCLUSION: Our findings demonstrate and confirm that coils tend to be located near the aneurysm periphery when few are inserted. However, when more coils are added, the radial distribution becomes more homogeneous. Coils are homogeneously distributed along the longitudinal axis

Analysis and quantification of endovascular coil distribution inside saccular aneurysms using histological images

OBJECTIVE: Endovascular coiling is often performed first by placing coils along the aneurysm wall to create a frame and then by filling up the aneurysm core. However, little attention has been paid to quantify this filling strategy and to see how it changes for different packing densities. The purpose of this work is to analyze and quantify endovascular coil distribution inside aneurysms based on serial histological images of experimental aneurysms. METHOD: Seventeen histological images from ten elastase-induced saccular aneurysms in rabbits treated with coils were studied. In-slice coil density, defined as the area taken up by coil winds, was calculated on each histological image. Images were analyzed by partitioning the aneurysm along its longitudinal and radial axis. Coil distribution was quantified by measuring and comparing the in-slice coil density of each partition. RESULTS: Mean total in-slice coil density was 22.0% ± 6.2% (range 10.1% to 30.2%). The density was non-significantly different (p=0.465) along the longitudinal axis. A significant difference (p<0.001) between peripheral and core densities was found. Additionally, peripheral-core density ratio was observed to be inversely proportional to the total in-slice coil density (R(2)=0.57, p<0.001). This ratio was near unity for high in-slice coil density (around 30%). CONCLUSION: Our findings demonstrate and confirm that coils tend to be located near the aneurysm periphery when few are inserted. However, when more coils are added, the radial distribution becomes more homogeneous. Coils are homogeneously distributed along the longitudinal axis

Differential Gene Expression in Well-healed and Poorly Healed Experimental Aneurysms after Coil Treatment1

Increased gene expression in densely packed aneurysms was associated with adhesion molecules, proteases, and cytokines in the rabbit aneurysm model; loosely packed aneurysms showed increased expression of multiple structural molecules, including collagens

Molecular Indices of Apoptosis Activation in

The online version of this article, along with updated information and services, is located on th

Acute ischemic stroke secondary to cardiac embolus of a ‘foreign body’ material after a redo sternotomy for mitral valve replacement: A case report

Cardiac surgery has been shown to be associated with increased risk of acute ischemic stroke. This report presents a case of a successful mechanical embolectomy procedure to treat a patient for an acute ischemic stroke, which was caused by the cardiac embolization of a ‘foreign body’ containing debris following a redo sternotomy procedure for mitral valve replacement and tricuspid valve annuloplasty.This work was supported by the National Institutes of Health (R01 NS105853), Science Foundation Ireland (13/RC/2073) and our industrial partners Cerenovus