ArticleScientific data2025
An ultra-wide-field fundus image dataset for intelligent diagnosis of intraocular tumors.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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.
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Who cites it
1 citing paper in PubMed.
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Authors and funding
7 authors.
Funding
Abstract
Retinal fundus photographs are now widely used in developing artificial intelligence (AI) systems for the detection of various fundus diseases. However, the application of AI algorithms in intraocular tumors remains limited due to the scarcity of large, publicly available, and diverse multi-disease fundus image datasets. Ultra-wide-field (UWF) fundus images, with a broad imaging range, offer unique advantages for early detection of intraocular tumors. In this study, we developed a comprehensive dataset containing 2,031 UWF fundus images, encompassing five distinct types of intraocular tumors and normal images. These images were classified by three expert annotators to ensure accurate labeling. This dataset aims to provide a robust, multi-disease, real-world data source to facilitate the development and validation of AI models for the early diagnosis and classification of intraocular tumors.
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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 Socratic graph.