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Artificial Intelligence in Radiation Therapy

Authors :
Yabo Fu
Per-Ivar Lønne
Tian Liu
Hao Zhang
Xiaofeng Yang
Ibrahim Hadzic
Leonard Wee
Suraj Pai
Eric D. Morris
Carri K Glide-Hurst
Alberto Traverso
Chenyang Shen
RS: GROW - R2 - Basic and Translational Cancer Biology
Radiotherapie
RS: GROW - R3 - Innovative Cancer Diagnostics & Therapy
Source :
IEEE Trans Radiat Plasma Med Sci, IEEE Transactions on Radiation and Plasma Medical Sciences, 6(2), 158-181. IEEE
Publication Year :
2022
Publisher :
IEEE, 2022.

Abstract

Artificial intelligence (AI) has great potential to transform the clinical workflow of radiotherapy. Since the introduction of deep neural networks (DNNs), many AI-based methods have been proposed to address challenges in different aspects of radiotherapy. Commercial vendors have started to release AI-based tools that can be readily integrated to the established clinical workflow. To show the recent progress in AI-aided radiotherapy, we have reviewed AI-based studies in five major aspects of radiotherapy, including image reconstruction, image registration, image segmentation, image synthesis, and automatic treatment planning. In each section, we summarized and categorized the recently published methods, followed by a discussion of the challenges, concerns, and future development. Given the rapid development of AI-aided radiotherapy, the efficiency and effectiveness of radiotherapy in the future could be substantially improved through intelligent automation of various aspects of radiotherapy.

Details

Language :
English
ISSN :
24697303 and 24697311
Volume :
6
Issue :
2
Database :
OpenAIRE
Journal :
IEEE Transactions on Radiation and Plasma Medical Sciences
Accession number :
edsair.doi.dedup.....6edf2198be56554aa92f24dd84164942