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建立基于引入深度学习的高精度超声波诊断方法

Masaaki Komatsu, Reina Komatsu, Akira Sakai

IEEE reviews in biomedical engineering

|December 31, 2025

概括

深度学习 (DL) 通过解决操作员依赖和工件来增强超声波诊断. 战略实施是最大限度地使患者在临床环境中受益的关键.

科学领域:

医疗成像医学成像
人工智能在医学中的应用
超声波技术超声波技术超声波技术

背景情况:

超声波成像提供了诸如可负担性和非侵入性等优势,但受到操作员依赖和人工制造的损害.
深度学习 (DL) 正在成为克服这些超声波局限性的解决方案.
配备DL的设备正在获得瘤学和心脏病学等领域的临床批准.

研究的目的:

组织和正式化超声波诊断局限性.
解释解决这些局限性的DL方法,包括原则,实施和评估.
检查DL在改善超声波工作流程中的临床应用和证据.

主要方法:

对超声波中DL的现有文献进行系统审查.
超声波限制的分类和相应的DL解决方案.
对临床应用,批准的设备和支持证据的分析.
对DL挑战进行批判性评估,并制定实施标准.

主要成果:

在研究之外,DL对改善临床超声波工作流程的实质性实用性得到了证明.
最近的DL应用程序和批准的设备在瘤学和心脏病学方面表现有前途.
有证据支持DL在提高诊断准确性和效率方面的作用.

结论:

对DL的战略实施对于最大限度地使患者受益并克服泛化和安全等挑战至关重要.
本综述为评估临床超声波环境中的DL提供了一个框架.
未来的研究应该专注于解决剩余的挑战,并进一步验证DL应用.

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