基于机器学习的方法来区分儿科的病毒性和细菌性肺炎:一个范围审查
Declan Rickard1, Muhammad Ashad Kabir2, Nusrat Homaira3
1School of Clinical Medicine, UNSW Sydney, Kensington, NSW, 2052, Australia.
Computer methods and programs in biomedicine
|May 11, 2025
概括
机器学习 (ML) 在从X射线分类儿童肺炎方面表现有前途,但依赖单个数据集限制了现实世界的使用. 未来的研究需要多样化的数据,以便在临床环境中可靠的AI.
科学领域:
- 人工智能的人工智能
- 医疗成像医学成像
- 儿科医学 儿科医学
背景情况:
- 肺炎是5岁以下儿童死亡的主要原因之一.
- 区分病毒和细菌性肺炎对于治疗至关重要,但在临床上具有挑战性.
- 机器学习 (ML) 和深度学习 (DL) 提供了使用胸部X射线 (CXR) 图像对肺炎进行分类的潜力.
研究的目的:
- 进行对ML技术进行范围审查,以使用CXR对儿童病毒性和细菌性肺炎进行分类.
- 总结有关这些ML模型的性能和局限性的当前证据.
主要方法:
- 在PubMed,Embase和Scopus进行了全面的文献搜索.
- 研究包括儿童 (0-18岁) 通过CXR诊断的肺炎,使用ML进行分类.
- 数据提取集中在ML模型,数据集和性能指标上,遵循PRISMA-ScR指南.
主要成果:
- 审查了35项研究 (2018-2025年),其中大多数 (31) 使用Kermany数据集,引发了概括性问题.
- 卷积神经网络 (CNN) 是主要使用的ML模型 (33项研究).
- 报告的准确度中位数为二进制 (病毒与细菌) 的92.3%,多类分类的91.8%,但具有显著的变化.
结论:
- 目前用于肺炎分类的ML模型受到依赖单一数据集和方法变化的限制.
- 由于这些限制,研究结果缺乏通用性和临床适用性.
- 未来的研究必须优先考虑多样化的数据集和标准化的报告,以便在儿科中获得可靠和可重复的AI工具.
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