A Survey of Publicly Available MRI Datasets for Potential Use in Artificial Intelligence Research

Artificial intelligence (AI) has the potential to bring transformative improvements to the field of radiology; yet, there are barriers to widespread clinical adoption. One of the most important barriers has been access to large, well‐annotated, widely representative medical image datasets, which can...

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Bibliographic Details
Published in:Journal of magnetic resonance imaging 2024-02, Vol.59 (2), p.450-480
Main Authors: Dishner, Katharine A., McRae‐Posani, Bala, Bhowmik, Arka, Jochelson, Maxine S., Holodny, Andrei, Pinker, Katja, Eskreis‐Winkler, Sarah, Stember, Joseph N.
Format: Article
Language:English
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Artificial Intelligence
Brain - diagnostic imaging
Datasets
Humans
Magnetic Resonance Imaging
Medical imaging
open‐access
public
Radiology
Radiology - methods
Spine
Surveys
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Summary:Artificial intelligence (AI) has the potential to bring transformative improvements to the field of radiology; yet, there are barriers to widespread clinical adoption. One of the most important barriers has been access to large, well‐annotated, widely representative medical image datasets, which can be used to accurately train AI programs. Creating such datasets requires time and expertise and runs into constraints around data security and interoperability, patient privacy, and appropriate data use. Recognizing these challenges, several institutions have started curating and providing publicly available, high‐quality datasets that can be accessed by researchers to advance AI models. The purpose of this work was to review the publicly available MRI datasets that can be used for AI research in radiology. Despite being an emerging field, a simple internet search for open MRI datasets presents an overwhelming number of results. Therefore, we decided to create a survey of the major publicly accessible MRI datasets in different subfields of radiology (brain, body, and musculoskeletal), and list the most important features of value to the AI researcher. To complete this review, we searched for publicly available MRI datasets and assessed them based on several parameters (number of subjects, demographics, area of interest, technical features, and annotations). We reviewed 110 datasets across sub‐fields with 1,686,245 subjects in 12 different areas of interest ranging from spine to cardiac. This review is meant to serve as a reference for researchers to help spur advancements in the field of AI for radiology. Level of Evidence Level 4 Technical Efficacy Stage 6
ISSN:1053-1807
1522-2586
1522-2586
DOI:10.1002/jmri.29101