ArticleScientific reports2018
A machine learning approach for automated assessment of retinal vasculature in the oxygen induced retinopathy model.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 17 citations in OpenAlex.
- Semi-automated quantification of total and avascular retinal area in the rat model of oxygen-induced retinopathy.Angiogenesis · 2026Article
- Automated Deep Learning Quantification of Avascular Area and Intravitreal Neovascularization in Retinal Flatmounts of Rodent Oxygen-Induced Retinopathy Models.Translational vision science & technology · 2026Article
- Nanomaterial-enabled delivery of plant-derived bioactive metabolites for diabetic retinopathy: from evidence appraisal to preclinical translation.Frontiers in pharmacology · 2026Review
- Review
- Retinopathy of Prematurity in Late Preterm Twins with a Birth Weight Discordance: Can it be Predicted by Artificial Intelligence?Beyoglu eye journal · 2023Article
- Open-source analysis and visualization of segmented vasculature datasets with VesselVio.Cell reports methods · 2022Article
- Big Data Research in Neuro-Ophthalmology: Promises and Pitfalls.Journal of neuro-ophthalmology : the official journal of the North American Neuro-Ophthalmology Society · 2019Review
- Engineering the vasculature for islet transplantation.Acta biomaterialia · 2019Review
- Vertebral artery fusiform aneurysm geometry in predicting rupture risk.Royal Society open science · 2018Article
- Automatic analysis of the retinal avascular area in the rat oxygen-induced retinopathy model.Molecular vision · 2018Article
- Development of a Semi-automated Computer-based Tool for the Quantification of Vascular Tortuosity in the Murine Retina.Ophthalmology scienceArticle
- Applications of Deep Learning: Automated Assessment of Vascular Tortuosity in Mouse Models of Oxygen-Induced Retinopathy.Ophthalmology scienceArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
Abstract
Preclinical studies of vascular retinal diseases rely on the assessment of developmental dystrophies in the oxygen induced retinopathy rodent model. The quantification of vessel tufts and avascular regions is typically computed manually from flat mounted retinas imaged using fluorescent probes that highlight the vascular network. Such manual measurements are time-consuming and hampered by user variability and bias, thus a rapid and objective method is needed. Here, we introduce a machine learning approach to segment and characterize vascular tufts, delineate the whole vasculature network, and identify and analyze avascular regions. Our quantitative retinal vascular assessment (QuRVA) technique uses a simple machine learning method and morphological analysis to provide reliable computations of vascular density and pathological vascular tuft regions, devoid of user intervention within seconds. We demonstrate the high degree of error and variability of manual segmentations, and designed, coded, and implemented a set of algorithms to perform this task in a fully automated manner. We benchmark and validate the results of our analysis pipeline using the consensus of several manually curated segmentations using commonly used computer tools. The source code of our implementation is released under version 3 of the GNU General Public License ( https://www.mathworks.com/matlabcentral/fileexchange/65699-javimazzaf-qurva ).
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.