近距离放射治疗的人工智能和深度学习

IF 2.6 3区 医学 Q3 ONCOLOGY
Xun Jia , Kevin Albuquerque
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引用次数: 0

摘要

近年来,人工智能(AI),特别是基于深度学习(DL)的方法,已被广泛应用于解决近距离治疗中的各种问题。本文对人工智能/深度学习技术在近距离治疗中不同领域的最新发展和应用进行了全面的文献综述,包括图像增强、配准、分割、治疗计划、质量保证、结果预测等。这篇综述将强调与外部放射治疗相比,解决近距离治疗独特挑战的研究。与此同时,尽管取得了令人兴奋的成就,但我们也注意到,我们仍处于使用AI/ dl技术来增强近距离治疗临床实践的早期阶段。因此,本文也将提出挑战和未来的方向。我们希望这篇综述能激发对这一主题的讨论,并引发未来有影响力的研究,将技术进步转化为医疗保健效益。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Artificial Intelligence and Deep Learning for Brachytherapy

In recent years, Artificial intelligence (AI), specifically deep-learning (DL) based methods, have been employed extensively to solve various problems in brachytherapy. This paper presents a comprehensive literature review on recent developments and applications of AI/DL technologies for different areas in brachytherapy, including image enhancement, registration, segmentation, treatment planning, quality assurance, outcome prediction, etc. The review will emphasize studies addressing unique challenges in brachytherapy, as compared to external beam radiotherapy. Meanwhile, despite exciting achievements, it is also noted that we are still at the early stage of employing AI/DL-technologies to enhance brachytherapy clinical practice. Hence, this paper will also present challenges and future directions. We hope this review will inspire discussions on this topic and trigger future impactful studies to transform technology advancements into healthcare benefits.

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来源期刊
CiteScore
5.80
自引率
0.00%
发文量
48
审稿时长
>12 weeks
期刊介绍: Each issue of Seminars in Radiation Oncology is compiled by a guest editor to address a specific topic in the specialty, presenting definitive information on areas of rapid change and development. A significant number of articles report new scientific information. Topics covered include tumor biology, diagnosis, medical and surgical management of the patient, and new technologies.
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