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End-to-end reproducible AI pipelines in radiology using the cloud

Authors :
Dennis Bontempi
Leonard Nuernberg
Suraj Pai
Deepa Krishnaswamy
Vamsi Thiriveedhi
Ahmed Hosny
Raymond H. Mak
Keyvan Farahani
Ron Kikinis
Andrey Fedorov
Hugo J. W. L. Aerts
Source :
Nature Communications, Vol 15, Iss 1, Pp 1-9 (2024)
Publication Year :
2024
Publisher :
Nature Portfolio, 2024.

Abstract

Abstract Artificial intelligence (AI) algorithms hold the potential to revolutionize radiology. However, a significant portion of the published literature lacks transparency and reproducibility, which hampers sustained progress toward clinical translation. Although several reporting guidelines have been proposed, identifying practical means to address these issues remains challenging. Here, we show the potential of cloud-based infrastructure for implementing and sharing transparent and reproducible AI-based radiology pipelines. We demonstrate end-to-end reproducibility from retrieving cloud-hosted data, through data pre-processing, deep learning inference, and post-processing, to the analysis and reporting of the final results. We successfully implement two distinct use cases, starting from recent literature on AI-based biomarkers for cancer imaging. Using cloud-hosted data and computing, we confirm the findings of these studies and extend the validation to previously unseen data for one of the use cases. Furthermore, we provide the community with transparent and easy-to-extend examples of pipelines impactful for the broader oncology field. Our approach demonstrates the potential of cloud resources for implementing, sharing, and using reproducible and transparent AI pipelines, which can accelerate the translation into clinical solutions.

Subjects

Subjects :
Science

Details

Language :
English
ISSN :
20411723
Volume :
15
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Nature Communications
Publication Type :
Academic Journal
Accession number :
edsdoj.2cae5f3512684eb6ada92f6f175be401
Document Type :
article
Full Text :
https://doi.org/10.1038/s41467-024-51202-2