Artificial Intelligence in Radiation Therapy

Yabo Fu, Hao Zhang, Eric D. Morris, Carri K. Glide-Hurst, Suraj Pai, Alberto Traverso, Leonard Wee, Ibrahim Hadzic, Per Ivar Lonne, Chenyang Shen, Tian Liu, Xiaofeng Yang

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

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.

Original languageEnglish
Pages (from-to)158-181
Number of pages24
JournalIEEE Transactions on Radiation and Plasma Medical Sciences
Volume6
Issue number2
DOIs
StatePublished - Feb 1 2022

Keywords

  • Artificial intelligence (AI)
  • image reconstruction
  • image registration
  • image segmentation
  • image synthesis
  • radiotherapy
  • treatment planning

Fingerprint

Dive into the research topics of 'Artificial Intelligence in Radiation Therapy'. Together they form a unique fingerprint.

Cite this