A fundus image dataset for intelligent retinopathy of prematurity system.
Journal
Scientific data
ISSN: 2052-4463
Titre abrégé: Sci Data
Pays: England
ID NLM: 101640192
Informations de publication
Date de publication:
27 May 2024
27 May 2024
Historique:
received:
13
12
2023
accepted:
10
05
2024
medline:
28
5
2024
pubmed:
28
5
2024
entrez:
27
5
2024
Statut:
epublish
Résumé
Image-based artificial intelligence (AI) systems stand as the major modality for evaluating ophthalmic conditions. However, most of the currently available AI systems are designed for experimental research using single-central datasets. Most of them fell short of application in real-world clinical settings. In this study, we collected a dataset of 1,099 fundus images in both normal and pathologic eyes from 483 premature infants for intelligent retinopathy of prematurity (ROP) system development and validation. Dataset diversity was visualized with a spatial scatter plot. Image classification was conducted by three annotators. To the best of our knowledge, this is one of the largest fundus datasets on ROP, and we believe it is conducive to the real-world application of AI systems.
Identifiants
pubmed: 38802420
doi: 10.1038/s41597-024-03362-5
pii: 10.1038/s41597-024-03362-5
doi:
Types de publication
Dataset
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
543Subventions
Organisme : National Natural Science Foundation of China (National Science Foundation of China)
ID : 82271103
Organisme : National Natural Science Foundation of China (National Science Foundation of China)
ID : 82301269
Informations de copyright
© 2024. The Author(s).
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