Lessons Learned in Building Expertly Annotated Multi-Institution Datasets and Hosting the RSNA AI Challenges.
Document Type
Article
Publication Date
5-1-2024
Original Citation
Kitamura F,
Prevedello L,
Colak E,
Halabi S,
Lungren M,
Ball R,
Kalpathy-Cramer J,
Kahn C,
Richards T,
Talbott J,
Shih G,
Lin H,
Andriole K,
Vazirabad M,
Erickson B,
Flanders A,
Mongan J.
Lessons Learned in Building Expertly Annotated Multi-Institution Datasets and Hosting the RSNA AI Challenges. Radiol Artif Intell. 2024 May;6(3):e230227.
Keywords
JMG, Humans, Artificial Intelligence, Radiology, Diagnostic Imaging, Societies, Medical, North America
JAX Source
Radiol Artif Intell. 2024 May;6(3):e230227.
ISSN
2638-6100
PMID
38477659
DOI
https://doi.org/10.1148/ryai.230227
Abstract
The Radiological Society of North America (RSNA) has held artificial intelligence competitions to tackle real-world medical imaging problems at least annually since 2017. This article examines the challenges and processes involved in organizing these competitions, with a specific emphasis on the creation and curation of high-quality datasets. The collection of diverse and representative medical imaging data involves dealing with issues of patient privacy and data security. Furthermore, ensuring quality and consistency in data, which includes expert labeling and accounting for various patient and imaging characteristics, necessitates substantial planning and resources. Overcoming these obstacles requires meticulous project management and adherence to strict timelines. The article also highlights the potential of crowdsourced annotation to progress medical imaging research. Through the RSNA competitions, an effective global engagement has been realized, resulting in innovative solutions to complex medical imaging problems, thus potentially transforming health care by enhancing diagnostic accuracy and patient outcomes.