ArticleJournal of digital imaging2021
Assistive Framework for Automatic Detection of All the Zones in Retinopathy of Prematurity Using Deep Learning.
Article in Journal of digital imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 37 citations in OpenAlex.
- Optimizing Field-of-View for Type-1 Retinopathy of Prematurity via Multiple Instance Learning.Translational vision science & technology · 2026Article
- Research on the construction of an AI diagnostic model for plus disease of retinopathy of prematurity based on cross-center fusion datasets.Frontiers in pediatrics · 2026Article
- Classification of Stages 1,2,3 and Preplus, Plus disease of ROP using MultiCNN_LSTM classifier.MethodsX · 2025Article
- COph100: A comprehensive fundus image registration dataset from infants constituting the "RIDIRP" database.Scientific data · 2025Article
- HVDROPDB datasets for research in retinopathy of prematurity.Data in brief · 2024Article
- Automatic zoning for retinopathy of prematurity with a key area location system.Biomedical optics express · 2024Article
- Fairness and generalisability in deep learning of retinopathy of prematurity screening algorithms: a literature review.BMJ open ophthalmology · 2023Review
- Research progress on diagnosing retinal vascular diseases based on artificial intelligence and fundus images.Frontiers in cell and developmental biology · 2023Review
- ADS-Net: attention-awareness and deep supervision based network for automatic detection of retinopathy of prematurity.Biomedical optics express · 2022Article
- Unsupervised Deep Anomaly Detection for Medical Images Using an Improved Adversarial Autoencoder.Journal of digital imaging · 2022Article
- Automatic zoning for retinopathy of prematurity with semi-supervised feature calibration adversarial learning.Biomedical optics express · 2022Article
- AFENet: Attention Fusion Enhancement Network for Optic Disc Segmentation of Premature Infants.Frontiers in neuroscience · 2022Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Retinopathy of prematurity (ROP) is a potentially blinding disorder seen in low birth weight preterm infants. In India, the burden of ROP is high, with nearly 200,000 premature infants at risk. Early detection through screening and treatment can prevent this blindness. The automatic screening systems developed so far can detect "severe ROP" or "plus disease," but this information does not help schedule follow-up. Identifying vascularized retinal zones and detecting the ROP stage is essential for follow-up or discharge from screening. There is no automatic system to assist these crucial decisions to the best of the authors' knowledge. The low contrast of images, incompletely developed vessels, macular structure, and lack of public data sets are a few challenges in creating such a system. In this paper, a novel method using an ensemble of "U-Network" and "Circle Hough Transform" is developed to detect zones I, II, and III from retinal images in which macula is not developed. The model developed is generic and trained on mixed images of different sizes. It detects zones in images of variable sizes captured by two different imaging systems with an accuracy of 98%. All images of the test set (including the low-quality images) are considered. The time taken for training was only 14 min, and a single image was tested in 30 ms. The present study can help medical experts interpret retinal vascular status correctly and reduce subjective variation in diagnosis.
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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.