对医疗超声波成像强化学习的全面审查
Hanae Elmekki1, Saidul Islam1, Ahmed Alagha1
1Concordia Institute for Information Systems Engineering, Concordia University, Montreal, Canada.
Artificial intelligence review
|June 26, 2025
概括
本调查探讨了人工智能 (AI),特别是强化学习 (RL) 如何创建自主医疗超声波 (US) 系统. 它提出了一个新的框架,将RL的进步映射到整个美国成像过程中,解决目前的局限性.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 强化学习是一种强化学习.
背景情况:
- 医学超声波 (美国) 成像因其临床优势而越来越受欢迎,但面临着操作员依赖和专家可用性有限等挑战.
- 现有的美国成像AI解决方案往往侧重于部分自主性,缺乏对整个成像过程的全面视图.
- 越来越需要自主系统来提高效率并克服美国解释的局限性.
研究的目的:
- 综合审查强化学习 (RL) 进展和医学超声波 (US) 成像过程的各个阶段的交叉点.
- 提出一种新的分类学,将美国成像工作流与RL开发管道整合起来.
- 确定当前的研究,局限性,以及在自主美国解决方案中RL的未来机会.
主要方法:
- 在RL和美国成像的交叉点上对现有文献进行系统审查.
- 开发一个全面的分类系统,将美国成像阶段映射到RL开发阶段 (数据准备,问题制定,模拟,培训,验证,微调).
- 在拟议的分类学中分析当前的研究工作.
主要成果:
- 确定了当前调查中的一个差距,重点是针对美国成像的部分自主AI解决方案.
- 提出了一种新的分类学,系统地将美国的成像过程与RL开发管道整合在一起.
- 强调RL在开发完全自主美国系统方面的潜力.
结论:
- 强化学习 (RL) 对推进自主医疗超声波 (US) 成像系统具有重大潜力.
- 拟议的分类为理解和推进美国成像中的RL应用提供了一个结构化的方法.
- 需要进一步的研究来解决局限性,并充分实现RL在这个领域的能力.
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