人工智能算法在使用咳声音诊断中的预测能力:系统性审查
Aya Abuelgasim Ibrahim Abdelhalim1, Hanady Me M Osman2, Sally Ibrahim Hafez Sadaka3
1Emergency Medicine, Najran Armed Forces Hospital, Ministry of Defense Health Services, Najran, SAU.
Cureus
|August 25, 2025
概括
人工智能 (AI) 对咳声音的分析显示为诊断儿童呼吸系统疾病的前景. 这种非侵入性方法提供了高精度,在资源有限的环境中尤其有价值.
科学领域:
- 儿童肺病学
- 医疗信息学
- 医学中的人工智能
背景情况:
- 呼吸系统疾病是全球儿童死亡的主要原因.
- 诊断儿童的呼吸系统疾病是一个挑战, 特别是在资源乏的地区.
- 人工智能 (AI) 提供了一种使用咳声音分析的新型非侵入性方法.
研究的目的:
- 系统地审查基于咳声音的儿童呼吸系统疾病的AI算法的诊断性能.
- 评估人工智能驱动的咳分析的临床适用性和方法质量.
- 综合人工智能在资源有限的环境中作为诊断工具的潜力.
主要方法:
- 按照PRISMA 2020指南进行系统文献审查.
- 包括来自主要数据库 (PubMed,Scopus,科学网络,IEEE Xplore) 的六项研究.
- 分析人工智能模型,包括后勤回归,CNN,SVM和混合方法;使用QUADAS-2进行评估.
主要成果:
- 人工智能模型显示出高诊断潜力,灵敏度为82%至94%,特异性为71%至91%.
- 一些人工智能方法超过了传统的WHO临床算法.
- 大多数研究的方法限制包括回顾性设计,小样本大小和缺乏外部验证.
结论:
- 基于人工智能的咳声音分析是诊断儿童呼吸系统疾病的有希望,可扩展的工具.
- 这项技术对于改善资源有限的诊断环境具有特别的价值.
- 进一步的研究需要前性验证和临床工作流程集成,以便在现实世界中应用.
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