ArticleScientific data2024
FARFUM-RoP, A dataset for computer-aided detection of Retinopathy of Prematurity.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- An explainable biomedical foundation model via large-scale concept-enhanced vision-language pretraining.Nature biomedical engineering · 2026Article
- Clinical Performance and Implementation of AI-Enabled Paediatric Ophthalmic Screening, Triage, Diagnosis, and Surveillance in Primary, Community, and Referral-Linked Pathways: A Systematic Review.Diagnostics (Basel, Switzerland) · 2026Review
- VEA-net: vascular enhancement attention with dual-backbone multi-task learning for comprehensive ROP management across multi-center datasets.Frontiers in cell and developmental biology · 2026Article
- Why aren't we using AI in eye clinics? A systematic review of barriers and solutions in AI-based fundus image diagnostics for ocular diseases.International journal of ophthalmology · 2026Review
- 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
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Authors and funding
8 authors.
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Abstract
Retinopathy of Prematurity (ROP) is a critical eye disorder affecting premature infants, characterized by abnormal blood vessel development in the retina. Plus Disease, indicating severe ROP progression, plays a pivotal role in diagnosis. Recent advancements in Artificial Intelligence (AI) have shown parity with or surpass human experts in ROP detection, especially Plus Disease. However, the success of AI systems depends on high-quality datasets, emphasizing the need for collaboration and data sharing among researchers. To address this challenge, the paper introduces a new public dataset, FARFUM-RoP (Farabi and Ferdowsi University of Mashhad's ROP dataset), comprising 1533 ROP fundus images from 68 patients, annotated independently by five experienced childhood ophthalmologists as "Normal," "Pre-Plus," or "Plus." Ethical principles and consent were meticulously followed during data collection. The paper presents the dataset structure, patient details, and expert labels.
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