Chen Jingchu, Qiu Richard L J, Wang Tonghe, Momin Shadab, Yang Xiaofeng
Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA.
School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA.
J Appl Clin Med Phys. 2025 Jun;26(6):e70034. doi: 10.1002/acm2.70034. Epub 2025 Feb 27.
Artificial intelligence (AI) has the potential to revolutionize brachytherapy's clinical workflow. This review comprehensively examines the application of AI, focusing on machine learning and deep learning, in various aspects of brachytherapy. We analyze AI's role in making brachytherapy treatments more personalized, efficient, and effective. The applications are systematically categorized into seven categories: imaging, preplanning, treatment planning, applicator reconstruction, quality assurance, outcome prediction, and real-time monitoring. Each major category is further subdivided based on cancer type or specific tasks, with detailed summaries of models, data sizes, and results presented in corresponding tables. Additionally, we discuss the limitations, challenges, and ethical concerns of current AI applications, along with perspectives on future directions. This review offers insights into the current advancements, challenges, and the impact of AI on treatment paradigms, encouraging further research to expand its clinical utility.
人工智能（AI）有潜力彻底改变近距离放射治疗的临床工作流程。本综述全面考察了人工智能（聚焦于机器学习和深度学习）在近距离放射治疗各个方面的应用。我们分析了人工智能在使近距离放射治疗更具个性化、高效性和有效性方面的作用。这些应用被系统地分为七类：成像、预计划、治疗计划、施源器重建、质量保证、结果预测和实时监测。每个主要类别根据癌症类型或特定任务进一步细分，并在相应表格中给出模型、数据规模和结果的详细总结。此外，我们讨论了当前人工智能应用的局限性、挑战和伦理问题，以及对未来方向的展望。本综述深入探讨了人工智能的当前进展、挑战及其对治疗模式的影响，鼓励开展进一步研究以扩大其临床应用价值。
