1,121 research outputs found
Supervised machine learning based multi-task artificial intelligence classification of retinopathies
Artificial intelligence (AI) classification holds promise as a novel and
affordable screening tool for clinical management of ocular diseases. Rural and
underserved areas, which suffer from lack of access to experienced
ophthalmologists may particularly benefit from this technology. Quantitative
optical coherence tomography angiography (OCTA) imaging provides excellent
capability to identify subtle vascular distortions, which are useful for
classifying retinovascular diseases. However, application of AI for
differentiation and classification of multiple eye diseases is not yet
established. In this study, we demonstrate supervised machine learning based
multi-task OCTA classification. We sought 1) to differentiate normal from
diseased ocular conditions, 2) to differentiate different ocular disease
conditions from each other, and 3) to stage the severity of each ocular
condition. Quantitative OCTA features, including blood vessel tortuosity (BVT),
blood vascular caliber (BVC), vessel perimeter index (VPI), blood vessel
density (BVD), foveal avascular zone (FAZ) area (FAZ-A), and FAZ contour
irregularity (FAZ-CI) were fully automatically extracted from the OCTA images.
A stepwise backward elimination approach was employed to identify sensitive
OCTA features and optimal-feature-combinations for the multi-task
classification. For proof-of-concept demonstration, diabetic retinopathy (DR)
and sickle cell retinopathy (SCR) were used to validate the supervised machine
leaning classifier. The presented AI classification methodology is applicable
and can be readily extended to other ocular diseases, holding promise to enable
a mass-screening platform for clinical deployment and telemedicine.Comment: Supplemental material attached at the en
Measurement of retinal blood vessel caliber using two different segmentation methods
In this paper, we explore two different retinal vessel segmentationmethods for the reliable estimation of vessels caliber in retinal images inorder to assess vascular changes as an aid for the diagnosis of the ocularmanifestations of several systemic diseases, namely diabetic retinopathyand hypertensive retinopathy
AutoMorph: Automated Retinal Vascular Morphology Quantification Via a Deep Learning Pipeline
Purpose: To externally validate a deep learning pipeline (AutoMorph) for automated analysis of retinal vascular morphology on fundus photographs. AutoMorph has been made publicly available, facilitating widespread research in ophthalmic and systemic diseases. Methods: AutoMorph consists of four functional modules: image preprocessing, image quality grading, anatomical segmentation (including binary vessel, artery/vein, and optic disc/cup segmentation), and vascular morphology feature measurement. Image quality grading and anatomical segmentation use the most recent deep learning techniques. We employ a model ensemble strategy to achieve robust results and analyze the prediction confidence to rectify false gradable cases in image quality grading. We externally validate the performance of each module on several independent publicly available datasets. Results: The EfficientNet-b4 architecture used in the image grading module achieves performance comparable to that of the state of the art for EyePACS-Q, with an F1-score of 0.86. The confidence analysis reduces the number of images incorrectly assessed as gradable by 76%. Binary vessel segmentation achieves an F1-score of 0.73 on AV-WIDE and 0.78 on DR HAGIS. Artery/vein scores are 0.66 on IOSTAR-AV, and disc segmentation achieves 0.94 in IDRID. Vascular morphology features measured from the AutoMorph segmentation map and expert annotation show good to excellent agreement. Conclusions: AutoMorph modules perform well even when external validation data show domain differences from training data (e.g., with different imaging devices). This fully automated pipeline can thus allow detailed, efficient, and comprehensive analysis of retinal vascular morphology on color fundus photographs. Translational Relevance: By making AutoMorph publicly available and open source, we hope to facilitate ophthalmic and systemic disease research, particularly in the emerging field of oculomics
Computational assessment of the retinal vascular tortuosity integrating domain-related information
[Abstract] The retinal vascular tortuosity presents a valuable potential as a clinical biomarker of many relevant vascular and systemic diseases. Commonly, the existent approaches face the tortuosity quantification by means of fully mathematical representations of the vessel segments. However, the specialists, based on their diagnostic experience, commonly analyze additional domain-related information that is not represented in these mathematical metrics of reference. In this work, we propose a novel computational tortuosity metric that outperforms the mathematical metrics of reference also incorporating anatomical properties of the fundus image such as the distinction between arteries and veins, the distance to the optic disc, the distance to the fovea, and the vessel caliber. The evaluation of its prognostic performance shows that the integration of the anatomical factors provides an accurate tortuosity assessment that is more adjusted to the specialists’ perception.Instituto de Salud Carlos II; DTS18/00136Ministerio de Ciencia, Innovación y Universidades; DPI2015-69948-RMinisterio de Ciencia, Innovación y Universidades; RTI2018-095894-B-I00Xunta de Galicia; ED431G/01Xunta de Galicia; ED431C 2016-04
Manual measurement of retinal bifurcation features
This paper introduces a new computerized tool for
accurate manual measurement of features of retinal bifurcation
geometry, designed for use in investigating correlations between measurement features and clinical conditions. The tool uses user-placed rectangles to measure the vessel width, and lines placed along vessel center lines to measure the angles. An
analysis is presented of measurements taken from 435 bifurcations.
These are compared with theoretical predictions based on
optimality principles presented in the literature. The new tool shows better agreement with the theoretical predictions than a simpler manual method published in the literature, but there remains a significant discrepancy between current theory and measured geometry
Automated Systems for Calculating Arteriovenous Ratio in Retinographies : A Scoping Review
There is evidence of an association between hypertension and retinal arteriolar narrowing. Manual measurement of retinal vessels comes with additional variability, which can be eliminated using automated software. This scoping review aims to summarize research on automated retinal vessel analysis systems. Searches were performed on Medline, Scopus, and Cochrane to find studies examining automated systems for the diagnosis of retinal vascular alterations caused by hypertension using the following keywords: diagnosis; diagnostic screening programs; image processing, computer-assisted; artificial intelligence; electronic data processing; hypertensive retinopathy; hypertension; retinal vessels; arteriovenous ratio and retinal image analysis. The searches generated 433 articles. Of these, 25 articles published from 2010 to 2022 were included in the review. The retinographies analyzed were extracted from international databases and real scenarios. Automated systems to detect alterations in the retinal vasculature are being introduced into clinical practice for diagnosis in ophthalmology and other medical specialties due to the association of such changes with various diseases. These systems make the classification of hypertensive retinopathy and cardiovascular risk more reliable. They also make it possible for diagnosis to be performed in primary care, thus optimizing ophthalmological visits
Retinal Artery/Vein Classification via Graph Cut Optimization
In many diseases with a cardiovascular component, the geometry of microvascular blood vessels changes. These changes are specific to arteries and veins, and can be studied in the microvasculature of the retina using retinal photography. To facilitate large-scale studies of artery/vein-specific changes in the retinal vasculature, automated classification of the vessels is required. Here we present a novel method for artery/vein classification based on local and contextual feature analysis of retinal vessels. For each vessel, local information in the form of a transverse intensity profile is extracted. Crossings and bifurcations of vessels provide contextual information. The local and contextual features are integrated into a non-submodular energy function, which is optimized exactly using graph cuts. The method was validated on a ground truth data set of 150 retinal fundus images, achieving an accuracy of 88.0% for all vessels and 94.0% for the six arteries and six veins with highest caliber in the image
Joint segmentation and classification of retinal arteries/veins from fundus images
Objective Automatic artery/vein (A/V) segmentation from fundus images is
required to track blood vessel changes occurring with many pathologies
including retinopathy and cardiovascular pathologies. One of the clinical
measures that quantifies vessel changes is the arterio-venous ratio (AVR) which
represents the ratio between artery and vein diameters. This measure
significantly depends on the accuracy of vessel segmentation and classification
into arteries and veins. This paper proposes a fast, novel method for semantic
A/V segmentation combining deep learning and graph propagation.
Methods A convolutional neural network (CNN) is proposed to jointly segment
and classify vessels into arteries and veins. The initial CNN labeling is
propagated through a graph representation of the retinal vasculature, whose
nodes are defined as the vessel branches and edges are weighted by the cost of
linking pairs of branches. To efficiently propagate the labels, the graph is
simplified into its minimum spanning tree.
Results The method achieves an accuracy of 94.8% for vessels segmentation.
The A/V classification achieves a specificity of 92.9% with a sensitivity of
93.7% on the CT-DRIVE database compared to the state-of-the-art-specificity and
sensitivity, both of 91.7%.
Conclusion The results show that our method outperforms the leading previous
works on a public dataset for A/V classification and is by far the fastest.
Significance The proposed global AVR calculated on the whole fundus image
using our automatic A/V segmentation method can better track vessel changes
associated to diabetic retinopathy than the standard local AVR calculated only
around the optic disc.Comment: Preprint accepted in Artificial Intelligence in Medicin
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