Artificial intelligence in radiotherapy

被引：67
作者：
Siddique, Sarkar [1 ]
Chow, James C. L. [2 ,3 ]
机构：
[1] Ryerson Univ, Dept Phys, Toronto, ON M5B 2K3, Canada
[2] Univ Hlth Network, Princess Margaret Canc Ctr, Radiat Med Program, Toronto, ON M5G 1X6, Canada
[3] Univ Toronto, Dept Radiat Oncol, Toronto, ON M5T 1P5, Canada
来源：
REPORTS OF PRACTICAL ONCOLOGY AND RADIOTHERAPY | 2020年 / 25卷 / 04期
关键词：
Artificial intelligence; Machine learning; Radiotherapy; Medical imaging; COMPUTER-AIDED DETECTION; SUPPORT VECTOR MACHINES; DEEP NEURAL-NETWORK; PROSTATE-CANCER; FEATURE-SELECTION; ERROR-DETECTION; RADIATION; KERNEL; BEAM; ALGORITHM;
D O I：
10.1016/j.rpor.2020.03.015
中图分类号：
R73 [肿瘤学];
学科分类号：
100214 ;
摘要：
Artificial intelligence (AI) has already been implemented widely in the medical field in the recent years. This paper first reviews the background of AI and radiotherapy. Then it explores the basic concepts of different AI algorithms and machine learning methods, such as neural networks, that are available to us today and how they are being implemented in radiotherapy and diagnostic processes, such as medical imaging, treatment planning, patient simulation, quality assurance and radiation dose delivery. It also explores the ongoing research on AI methods that are to be implemented in radiotherapy in the future. The review shows very promising progress and future for AI to be widely used in various areas of radiotherapy. However, basing on various concerns such as availability and security of using big data, and further work on polishing and testing AI algorithms, it is found that we may not ready to use AI primarily in radiotherapy at the moment. (C) 2020 Greater Poland Cancer Centre. Published by Elsevier B.V. All rights reserved.
引用
收藏
页码：656 / 666
页数：11
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