Artificial Intelligence for Radiation Oncology Applications Using Public Datasets

Kareem A. Wahid, Enrico Glerean, Jaakko Sahlsten, Joel Jaskari, Kimmo Kaski, Mohamed A. Naser, Renjie He, Abdallah S.R. Mohamed, Clifton D. Fuller*

*Corresponding author for this work

Research output: Contribution to journalReview Articlepeer-review

1 Citation (Scopus)
7 Downloads (Pure)

Abstract

Artificial intelligence (AI) has exceptional potential to positively impact the field of radiation oncology. However, large curated datasets - often involving imaging data and corresponding annotations - are required to develop radiation oncology AI models. Importantly, the recent establishment of Findable, Accessible, Interoperable, Reusable (FAIR) principles for scientific data management have enabled an increasing number of radiation oncology related datasets to be disseminated through data repositories, thereby acting as a rich source of data for AI model building. This manuscript reviews the current and future state of radiation oncology data dissemination, with a particular emphasis on published imaging datasets, AI data challenges, and associated infrastructure. Moreover, we provide historical context of FAIR data dissemination protocols, difficulties in the current distribution of radiation oncology data, and recommendations regarding data dissemination for eventual utilization in AI models. Through FAIR principles and standardized approaches to data dissemination, radiation oncology AI research has nothing to lose and everything to gain.

Original languageEnglish
Pages (from-to)400-414
JournalSeminars in Radiation Oncology
Volume32
Issue number4
DOIs
Publication statusPublished - Oct 2022
MoE publication typeA2 Review article in a scientific journal

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