FARFUM-RoP, A dataset for computer-aided detection of Retinopathy of Prematurity.

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2024-10-30 DOI:10.1038/s41597-024-03897-7

Morteza Akbari, Hamid-Reza Pourreza, Elias Khalili Pour, Afsar Dastjani Farahani, Fatemeh Bazvand, Nazanin Ebrahimiadib, Marjan Imani Fooladi, Fereshteh Ramazani K

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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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FARFUM-RoP，早产儿视网膜病变计算机辅助检测数据集。

早产儿视网膜病变（ROP）是早产儿的一种严重眼部疾病，其特点是视网膜血管发育异常。显示严重早产儿视网膜病变进展的 "加号病"（Plus Disease）在诊断中起着至关重要的作用。人工智能（AI）的最新进展表明，在 ROP 检测（尤其是加号病）方面，人工智能与人类专家不相上下，甚至更胜一筹。然而，人工智能系统的成功取决于高质量的数据集，这就强调了研究人员之间合作和数据共享的必要性。为了应对这一挑战，本文介绍了一个新的公共数据集 FARFUM-RoP（法拉比和马什哈德费尔道西大学的 ROP 数据集），该数据集由 68 名患者的 1533 张 ROP 眼底图像组成，由五位经验丰富的儿童眼科专家独立注释为 "正常"、"Pre-Plus "或 "Plus"。数据收集过程中严格遵守了伦理原则并征得了同意。本文介绍了数据集结构、患者详情和专家标签。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Scientific Data Social Sciences-Education

CiteScore

11.20

自引率

4.10%

发文量

689

审稿时长

16 weeks

期刊介绍： Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.