基于人工智能的自动化分析,用于在胸部超声波上检测肺溢液:系统性审查
Guido Marchi1, Luciano Gabbrielli1, Marco Gherardi1
1Pulmonology Unit, Cardiothoracic and Vascular Department, University Hospital of Pisa, Via Paradisa 2, 56124 Pisa, Italy.
Diagnostics (Basel, Switzerland)
|January 10, 2026
概括
人工智能 (AI) 辅助的胸部超声波 (TUS) 显示出检测多发液 (PE) 的前景. 然而,目前的证据受限于方法上的弱点,使其不足以用于常规的临床使用.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 肺部病理学 肺部病理学
背景情况:
- 胸腔溢液 (PE) 是一种常见的疾病,需要准确检测才能有效管理.
- 胸部超声波 (TUS) 是PE的主要诊断工具,因其安全性和可移植性而受到重视.
- 正在探索基于人工智能的TUS自动化分析,以提高诊断准确度和减少解释变化.
研究的目的:
- 评估人工智能辅助的TUS诊断准确度,以检测多流液.
- 综合当前关于人工智能算法在TUS中对PE诊断的性能的证据.
主要方法:
- 一个遵循PRISMA指南的系统审查,注册在PROSPERO.
- 在多个数据库中进行搜索 (MEDLINE,Scopus,Google Scholar等). 在2025年8月之前.
- 包括具有公认参考标准的研究;评估偏差风险 (QUADAS-2) 和确定性 (GRADE).
主要成果:
- 包括2021-2025年发表的五项研究 (7565名患者),利用各种卷积神经网络.
- 报告的灵敏度 (70.6-100%),特异性 (67-100%) 和AUC (0.77-0.99),对于小/复杂的输液和重症患者的性能变化.
- 研究显示,由于追溯设计和数据集分离不足,存在偏差的高风险;外部验证显示性能减弱.
结论:
- 人工智能辅助的TUS在受控环境中显示出PE检测的潜力,但显示出不一致的证据.
- 方法上的局限性,包括追溯设计和有限的验证,导致确定性低至中等.
- 在临床采用之前,需要进一步的强大,前性,多中心研究,独立验证.
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