ArticleData in brief2024
HVDROPDB datasets for research in retinopathy of prematurity.
Article in Data in brief, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 19 citations in OpenAlex.
- Automated diagnosis of plus form and early stages of ROP using deep learning models.Scientific reports · 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
- Macretina: a dataset, to support deep learning assisted retinopathy of prematurity diagnosis.Scientific reports · 2025Article
- A novel method of quantifying retinal vascular tortuosity in retinopathy of prematurity.Scientific reports · 2025Article
- Feasibility of Multimodal Deep Learning for Automated Staging of Familial Exudative Vitreoretinopathy Using Color Fundus Photographs and Fluorescein Angiography.Diagnostics (Basel, Switzerland) · 2025Article
- Classification of Stages 1,2,3 and Preplus, Plus disease of ROP using MultiCNN_LSTM classifier.MethodsX · 2025Article
- FARFUM-RoP, A dataset for computer-aided detection of Retinopathy of Prematurity.Scientific data · 2024Article
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Retinopathy of prematurity (ROP) is a retinal disorder that may bring about blindness in preterm infants. Early detection and treatment of ROP can prevent this blindness. The gold standard technique for ROP screening is indirect ophthalmoscopy performed by ophthalmologists. The scarcity of medical professionals and inter-observer heterogeneity in ROP grading are two of the screening concerns. Researchers employ artificial intelligence (AI) driven ROP screening systems to assist medical experts. A major hurdle in developing these systems is the unavailability of annotated data sets of fundus images. Anatomical landmarks in the retina, such as the optic disc, macula, blood vessels, and ridge, are used to identify ROP characteristics. HVDROPDB is the first dataset to be published for the retinal structure segmentation of fundus images of preterm infants. It is prepared from two diverse imaging systems on the Indian population for segmenting the lesions mentioned above and annotated by a group of ROP experts. Each dataset contains retinal fundus images of premature infants with the ground truths prepared manually to assist researchers in developing explainable automated screening systems.
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Registered trials
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