Applications of artificial intelligence for machine- and patient-specific quality assurance in radiation therapy: current status and future directions

Machine- and patient-specific quality assurance (QA) is essential to ensure the safety and accuracy of radiotherapy. QA methods have become complex, especially in high-precision radiotherapy such as intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT), and various...

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Bibliographic Details
Published in:Journal of radiation research 2024-07, Vol.65 (4), p.421-432
Main Authors: Ono, Tomohiro, Iramina, Hiraku, Hirashima, Hideaki, Adachi, Takanori, Nakamura, Mitsuhiro, Mizowaki, Takashi
Format: Article
Language:English
Subjects:
Artificial Intelligence
Humans
Machine learning
Oncology/Medicine
Quality Assurance, Health Care
Quality control
Radiation
Radiotherapy
Radiotherapy - methods
Radiotherapy Planning, Computer-Assisted - methods
Radiotherapy, Intensity-Modulated - methods
Technology application
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Summary:Machine- and patient-specific quality assurance (QA) is essential to ensure the safety and accuracy of radiotherapy. QA methods have become complex, especially in high-precision radiotherapy such as intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT), and various recommendations have been reported by AAPM Task Groups. With the widespread use of IMRT and VMAT, there is an emerging demand for increased operational efficiency. Artificial intelligence (AI) technology is quickly growing in various fields owing to advancements in computers and technology. In the radiotherapy treatment process, AI has led to the development of various techniques for automated segmentation and planning, thereby significantly enhancing treatment efficiency. Many new applications using AI have been reported for machine- and patient-specific QA, such as predicting machine beam data or gamma passing rates for IMRT or VMAT plans. Additionally, these applied technologies are being developed for multicenter studies. In the current review article, AI application techniques in machine- and patient-specific QA have been organized and future directions are discussed. This review presents the learning process and the latest knowledge on machine- and patient-specific QA. Moreover, it contributes to the understanding of the current status and discusses the future directions of machine- and patient-specific QA.
ISSN:0449-3060
1349-9157
1349-9157
DOI:10.1093/jrr/rrae033