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

Dennis Bontempi, Leonard Nuernberg, Suraj Pai, Deepa Krishnaswamy, Vamsi Thiriveedhi, Ahmed Hosny, Raymond H. Mak, Keyvan Farahani, Ron Kikinis, Andrey Fedorov and Hugo J. W. L. Aerts ()
Additional contact information
Dennis Bontempi: Harvard Medical School
Leonard Nuernberg: Harvard Medical School
Suraj Pai: Harvard Medical School
Deepa Krishnaswamy: Harvard Medical School
Vamsi Thiriveedhi: Harvard Medical School
Ahmed Hosny: Harvard Medical School
Raymond H. Mak: Harvard Medical School
Keyvan Farahani: National Heart, Lung, and Blood Institute, National Institutes of Health
Ron Kikinis: Harvard Medical School
Andrey Fedorov: Harvard Medical School
Hugo J. W. L. Aerts: Harvard Medical School

Nature Communications, 2024, vol. 15, issue 1, 1-9

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.

Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-024-51202-2 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-51202-2

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-024-51202-2

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().