儿童肺部声音分析的人工智能模型:系统审查和元分析
Ji Soo Park1, Sa-Yoon Park2,3, Jae Won Moon1
1Department of Pediatrics, Seoul National University College of Medicine, Seoul, Republic of Korea.
Journal of medical Internet research
|April 18, 2025
概括
机器学习模型在分析儿童肺部声音以检测喘等疾病时表现出高准确度. 然而,数据一致性和验证方面的挑战需要进一步的研究,以便广泛的临床使用.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能在医学中的应用
- 儿科肺病学 儿科肺病学
背景情况:
- 儿童呼吸道疾病是儿童疾病和死亡的重要原因.
- 传统的肺声音听觉是主观的,并且在临床医生之间有所不同.
- 人工智能和机器学习 (ML) 提供客观的,通过电子耳语镜对肺部声音进行自动分析.
研究的目的:
- 系统地审查和元分析儿童肺部声音分析中的ML模型的性能.
- 评估方法,模型性能和数据库特征.
- 确定在儿科呼吸道诊断中临床实施ML的局限性和未来方向.
主要方法:
- 在主要数据库 (PubMed,Embase,Web of Science等) 进行系统的文献搜索. 从1990年开始到2024年.
- 包括研究开发儿童肺声分类的ML模型,使用定义的数据库和医生标记的标准.
- 使用修改的 QUADAS-2 框架和二元分类任务的双变量元分析进行偏差风险评估.
主要成果:
- 41项研究符合纳入标准;大多数研究侧重于喘和异常肺声音的检测.
- 呼检测的聚合灵敏度和特异性分别为0.902和0.955.
- 卷积神经网络是常见的ML模型,但数据集和方法的高异质性限制了概括性.
结论:
- ML模型对准确的儿科肺部声音分析充满希望.
- 局限性包括数据集异质性,缺乏标准化指导方针,以及外部验证不足.
- 未来的研究应该优先考虑标准化协议和大型多中心数据集,以提高临床效用.
关键词:
异常的肺部声音检测 检测异常的肺部声音人工智能的人工智能是人工智能.喘 喘 是一种听术 (Auscultation) 是一种听觉方式.孩子们的孩子们的孩子们的孩子们.诊断 诊断 诊断 的 诊断 诊断 诊断 诊断 的 诊断肺部声音分析 肺部声音分析机器学习是机器学习.这就是MEL光谱图.疾病发病率 疾病发病率 疾病发病率死亡率 死亡率儿科 儿科 儿科肺炎是一种肺炎.呼吸系统疾病的分类呼吸道病理 呼吸道病理系统性审查 系统性审查喘息检测系统可以检测出喘.更多相关视频
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