Intelligent Radiation: A review of machine learning applications in nuclear and radiological sciences Review
| Authors: | Jinia, A. J.; Clarke, S. D.; Moran, J. M.; Pozzi, S. A. |
| Review Title: | Intelligent Radiation: A review of machine learning applications in nuclear and radiological sciences |
| Abstract: | Modern advancements in computing power and the ability of machine learning (ML) to model complex relationships between input and output have opened new prospects for data processing. This ML technology finds applications in nuclear and radiological sciences to extract meaningful information from data and drive intelligent decision-making. The literature review performed in the present manuscript encompasses key areas, including nuclear power, nuclear security, international safeguards, and the use of radiological sciences in healthcare. The applications discussed range from predictive modeling of nuclear processes to enhancing image reconstruction and analysis in medical imaging. The article also focuses on highlighting key studies and methodologies, offering a demonstration of various ML models in handling the unique challenges posed by nuclear and radiological data. The goal is to provide a comprehensive review, which can serve as a guide for researchers, providing a deeper understanding of the impact and potential of ML in the field. © 2024 The Authors |
| Keywords: | health care; image enhancement; medical imaging; decision making; image reconstruction; nuclear energy; machine learning; digital storage; machine-learning; data handling; nuclear power; nuclear security and international safeguards; radiation in healthcare; nuclear fuels; computing power; international safeguards; machine learning applications; model complexes; modern advancement; nuclear security; nuclear security and international safeguard; radiological science |
| Journal Title: | Annals of Nuclear Energy |
| Volume: | 201 |
| ISSN: | 0306-4549 |
| Publisher: | Pergamon-Elsevier Science Ltd |
| Date Published: | 2024-06-15 |
| Start Page: | 110444 |
| Language: | English |
| DOI: | 10.1016/j.anucene.2024.110444 |
| PROVIDER: | scopus |
| DOI/URL: | |
| Notes: | The MSK Cancer Center Support Grant (P30 CA008748) is acknowledged in the PDF -- Corresponding author is MSK authors: Abbas J. Jinia -- Source: Scopus |
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