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首页> 外文期刊>Technology in cancer research & treatment. >Artificial Intelligence in Radiotherapy Treatment Planning: Present and Future
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Artificial Intelligence in Radiotherapy Treatment Planning: Present and Future

机译：放射治疗计划规划中的人工智能：现今与未来

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Treatment planning is an essential step of the radiotherapy workflow. It has become more sophisticated over the past couple of decades with the help of computer science, enabling planners to design highly complex radiotherapy plans to minimize the normal tissue damage while persevering sufficient tumor control. As a result, treatment planning has become more labor intensive, requiring hours or even days of planner effort to optimize an individual patient case in a trial-and-error fashion. More recently, artificial intelligence has been utilized to automate and improve various aspects of medical science. For radiotherapy treatment planning, many algorithms have been developed to better support planners. These algorithms focus on automating the planning process and/or optimizing dosimetric trade-offs, and they have already made great impact on improving treatment planning efficiency and plan quality consistency. In this review, the smart planning tools in current clinical use are summarized in 3 main categories: automated rule implementation and reasoning, modeling of prior knowledge in clinical practice, and multicriteria optimization. Novel artificial intelligence–based treatment planning applications, such as deep learning–based algorithms and emerging research directions, are also reviewed. Finally, the challenges of artificial intelligence–based treatment planning are discussed for future works.
机译：治疗计划是放射治疗工作流程的重要步骤。在计算机科学的帮助下，在过去几十年中，它已经变得更加复杂，使规划人员能够设计高度复杂的放射疗法计划，以尽量减少正常的组织损伤，同时坚持充分的肿瘤控制。因此，治疗规划已成为更多的劳动密集，需要数小时甚至是计划者努力，以试验和错误方式优化个体患者案件。最近，人工智能已被利用自动化和改进医学科学的各个方面。对于放射治疗计划，许多算法已经开发给更好的支持策划者。这些算法专注于自动化规划过程和/或优化剂量折磨，并且它们对提高治疗规划效率和计划质量一致性产生了很大的影响。在本次审查中，当前临床用途的智能计划工具总结为3个主要类别：自动化规则实施和推理，临床实践中的先验知识和多标识优化的建模。还审查了新颖的基于人工智能的治疗计划应用，例如基于深度学习的算法和新兴的研究方向。最后，讨论了对未来的作品讨论了人工智能的治疗规划的挑战。

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