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Machine intelligence for radiation science: summary of the Radiation Research Society 67th annual meeting symposium.

In International journal of radiation biology ; h5-index 29.0

The era of high-throughput techniques created big data in the medical field and research disciplines. Machine intelligence (MI) approaches can overcome critical limitations on how those large-scale data sets are processed, analyzed, and interpreted. The 67th Annual Meeting of the Radiation Research Society featured a symposium on MI approaches to highlight recent advancements in the radiation sciences and their clinical applications. This article summarizes three of those presentations regarding recent developments for metadata processing and ontological formalization, data mining for radiation outcomes in pediatric oncology, and imaging in lung cancer.

Wilson Lydia J, Kiffer Frederico C, Berrios Daniel C, Bryce-Atkinson Abigail, Costes Sylvain V, Gevaert Olivier, Matarèse Bruno F E, Miller Jack, Mukherjee Pritam, Peach Kristen, Schofield Paul N, Slater Luke T, Langen Britta

2023-Feb-03

Machine learning, artificial intelligence, lung cancer, ontology, radiobiology, radiotherapy, voxel-based analysis