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

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

SUBMITTER: Bontempi D 

PROVIDER: S-EPMC11322541 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature