综述强化学习在医学超声中的应用，助力实现全自动超声检查。
Comprehensive Review of Reinforcement Learning for Medical Ultrasound Imaging
- 构建超声流程与强化学习开发的融合分类体系
- 梳理现有研究在扫描引导、器官识别等环节的进展
- 指出实现全自动超声的关键挑战，适合医疗AI研究者参考
近年来，医学超声因其低成本、便携性和实时性成为临床首选成像方式之一。然而，其应用受限于操作者依赖、解读差异和分辨率低等问题，且专业医师资源稀缺。这促使发展无需人工干预的自主系统以提升效率与吞吐量。强化学习（RL）作为人工智能的前沿方向，可通过环境交互中的奖励机制训练智能体完成复杂任务。现有超声领域的综述多聚焦部分自动化方案，如扫描引导、器官识别和图像诊断，但未系统结合超声全流程与强化学习发展路径。本文提出一种综合性分类框架，将超声流程阶段与强化学习开发流程相融合，不仅总结了当前在该领域的最新进展，还指出了实现完全自主超声系统所面临的未解挑战。本工作旨在全面回顾研究现状，揭示强化学习在构建全自动超声系统中的潜力，并明确未来发展的瓶颈与机遇。
原文摘要 · Abstract (English)
Medical Ultrasound (US) imaging has seen increasing demands over the past years, becoming one of the most preferred imaging modalities in clinical practice due to its affordability, portability, and real-time capabilities. However, it faces several challenges that limit its applicability, such as operator dependency, variability in interpretation, and limited resolution, which are amplified by the low availability of trained experts. This calls for the need of autonomous systems that are capable of reducing the dependency on humans for increased efficiency and throughput. Reinforcement Learning (RL) comes as a rapidly advancing field under Artificial Intelligence (AI) that allows the development of autonomous and intelligent agents that are capable of executing complex tasks through rewarded interactions with their environments. Existing surveys on advancements in the US scanning domain predominantly focus on partially autonomous solutions leveraging AI for scanning guidance, organ identification, plane recognition, and diagnosis. However, none of these surveys explore the intersection between the stages of the US process and the recent advancements in RL solutions. To bridge this gap, this review proposes a comprehensive taxonomy that integrates the stages of the US process with the RL development pipeline. This taxonomy not only highlights recent RL advancements in the US domain but also identifies unresolved challenges crucial for achieving fully autonomous US systems. This work aims to offer a thorough review of current research efforts, highlighting the potential of RL in building autonomous US solutions while identifying limitations and opportunities for further advancements in this field.
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