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人工智能在肿瘤放射治疗中的应用

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目的 对以深度学习为代表的人工智能在肿瘤放射治疗全程中的应用现状进行归纳,并对目前其存在的问题和发展前景进行阐述.方法 以"放射治疗、人工智能、深度学习、自动勾画、质量保证、图像配准"等为中文关键词,"radio-therapy,artificial intelligence,deep learning,automatic contouring,quality assurance,image registration"等为英文关键词,在PubMed、CNKI数据库中检索2012-2023发表的相关文献.纳入标准:(1)人工智能在放疗图像配准和自动勾画中的应用;(2)人工智能在放疗计划制定中的应用;(3)人工智能在放疗质量保证及疗效预测中的应用.排除标准:与放射治疗相关性较低.结果 人工智能中的深度学习技术在放射治疗的多个环节中扮演重要角色,尤其在医学图像处理方面,现有研究证明深度学习在图像合成与配准、靶区自动勾画领域可以减少放射科医生的工作量并提高结果的一致性.然而在放射治疗计划和质量保证中的应用仍需要进一步开发和临床验证以充分发挥其潜力.结论 人工智能与放射治疗的结合已经初步取得了一些成果,但仍面临着数据数量与质量、算法可靠性、道德伦理等诸多问题.因此人工智能应用到临床还需要不断优化算法以加速该领域发展.
Application of artificial intelligence in tumor radiotherapy
Objective To review the current status of artificial intelligence,represented by deep learning,in radiotherapy,and elaborate on the current problems and development prospects.Methods A literature search for the period from 2012 to 2023 was conducted in the PubMed and CNKI databases using the keywords"radiotherapy,artificial intelligence,deep learning,automatic contouring,quality assurance,and image registration".Inclusion criteria:(1)Application of artificial intelligence in radiotherapy image registration and automatic delineation;(2)The application of artificial intelligence in the formulation of radiotherapy plans;(3)The application of artificial intelligence in quality assurance and efficacy prediction of radiotherapy.Exclusion criteria:Literature with low relevance and credibility to radiation therapy.Finally,85 relevant articles were selected for analysis.Results Deep learning technologies within the realm of artificial intelligence have as-sumed a critical role across various stages of radiotherapy,particularly in the field of medical image processing.Current research demonstrates that deep learning can significantly reduce the workload of radiological physi-cians in the areas of image synthesis and registration,as well as in the automatic delineation of target areas,thereby en-hancing the consistency of the outcomes.However,the application of these technologies in the development of radiothera-py plans and in quality assurance still requires further refinement and clinical validation to fully realize their potential.Conclusions The combination of artificial intelligence and radiotherapy has made some initial achievements but still faces many challenges such as the quantity and quality of data,algorithm reliability,ethical and moral issues.Therefore,con-tinuous algorithm optimization is necessary for the application of artificial intelligence to clinical practice to accelerate the development of this field.

radiotherapyartificial intelligencedeep learningautomatic contouringquality assuranceimage registration

张琪月、李鸿岩、张红、徐度玲、柳佳娣

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中国科学院近代物理研究所医学物理研究室,甘肃兰州 730000

兰州大学核科学与技术学院,甘肃兰州 730000

中国科学院重离子辐射生物学与医学重点实验室,甘肃兰州 730000

甘肃省同位素实验室,甘肃兰州 730300

中国科学院大学核科学与技术学院,北京 100039

先进能源科学与技术广东省实验室,广东惠州 516006

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放疗 人工智能 深度学习 自动勾画 质量保证 图像配准

2024

中华肿瘤防治杂志
中华预防医学会 山东省肿瘤防治研究院

中华肿瘤防治杂志

CSTPCD北大核心
影响因子:1.292
ISSN:1673-5269
年,卷(期):2024.31(3)
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