Publicly Available Imaging Datasets for Age-related Macular Degeneration: Evaluation according to the Findable, Accessible, Interoperable, Reusable (FAIR) Principles.

IF 3 2区 医学 Q1 OPHTHALMOLOGY Experimental eye research Pub Date : 2025-03-13 DOI:10.1016/j.exer.2025.110342
Nayoon Gim, Alina Ferguson, Marian Blazes, Sanjay Soundarajan, Aydan Gasimova, Yu Jiang, Clarissa Sanchez Gutiérrez, Luca Zalunardo, Giulia Corradetti, Tobias Elze, Naoto Honda, Nadia Waheed, Anne Marie Cairns, M Valeria Canto-Soler, Amitha Dolmalpally, Mary Durbin, Daniela Ferrara, Jewel Hu, Prashant Nair, Aaron Y Lee, Srinivas R Sadda, Tiarnan D L Keenan, Bhavesh Patel, Cecilia S Lee
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Abstract

Age-related macular degeneration (AMD), a leading cause of vision loss among older adults, affecting more than 200 million people worldwide. With no cure currently available and a rapidly increasing prevalence, emerging approaches such as artificial intelligence (AI) and machine learning (ML) hold promise for advancing the study of AMD. The effective utilization of AI and ML in AMD research is highly dependent on access to high-quality and reusable clinical data. The Findable, Accessible, Interoperable, Reusable (FAIR) principles, published in 2016, provide a framework for sharing data that is easily usable by both humans and machines. However, it is unclear how these principles are implemented with regards to ophthalmic imaging datasets for AMD research. We evaluated openly available AMD-related datasets containing optical coherence tomography (OCT) data against the FAIR principles. The assessment revealed that none of the datasets were fully compliant with FAIR principles. Specifically, compliance rates were 5% for Findable, 82% for Accessible, 73% for Interoperable, and 0% for Reusable. The low compliance rates can be attributed to the relatively recent emergence of these principles and the lack of established standards for data and metadata formatting in the AMD research community. This article presents our findings and offers guidelines for adopting FAIR practices to enhance data sharing in AMD research.

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老年性黄斑变性(AMD)是导致老年人视力丧失的主要原因,影响着全球 2 亿多人。由于目前尚无治疗方法,且发病率迅速上升,人工智能(AI)和机器学习(ML)等新兴方法有望推动老年黄斑变性的研究。在 AMD 研究中有效利用人工智能和 ML 在很大程度上取决于能否获得高质量和可重复使用的临床数据。2016 年发布的 "可查找、可访问、可互操作、可重用(FAIR)"原则为人类和机器共享易于使用的数据提供了一个框架。然而,目前还不清楚如何将这些原则应用于眼科成像数据集的 AMD 研究。我们根据 FAIR 原则对包含光学相干断层扫描 (OCT) 数据的公开 AMD 相关数据集进行了评估。评估结果显示,没有一个数据集完全符合 FAIR 原则。具体来说,可查找的符合率为 5%,可访问的符合率为 82%,可互操作的符合率为 73%,可重复使用的符合率为 0%。符合率低的原因可能是这些原则的出现相对较晚,而且 AMD 研究界缺乏既定的数据和元数据格式标准。本文介绍了我们的研究结果,并为采用 FAIR 实践提供指导,以加强 AMD 研究中的数据共享。
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来源期刊
Experimental eye research
Experimental eye research 医学-眼科学
CiteScore
6.80
自引率
5.90%
发文量
323
审稿时长
66 days
期刊介绍: The primary goal of Experimental Eye Research is to publish original research papers on all aspects of experimental biology of the eye and ocular tissues that seek to define the mechanisms of normal function and/or disease. Studies of ocular tissues that encompass the disciplines of cell biology, developmental biology, genetics, molecular biology, physiology, biochemistry, biophysics, immunology or microbiology are most welcomed. Manuscripts that are purely clinical or in a surgical area of ophthalmology are not appropriate for submission to Experimental Eye Research and if received will be returned without review.
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