机器学习在识别结核病耐药性的准确性:系统性审查和元分析
XiaoBo Wei1,2, Norhashimah Mohd Norsuddin1, Hamzaini Bin Abdul Hamid3
1Centre of Diagnostic Imaging, Therapeutic and Investigative Studies (CODTIS), Faculty of Health Sciences, The National University of Malaysia (UKM) Kuala Lumpur Malaysia.
Health science reports
|October 16, 2025
概括
机器学习 (ML) 模型在诊断耐药结核病 (DR-TB) 中显示出高准确性. 深度学习 (DL) 在诊断方面表现出色,但对于临床应用而言,外部验证至关重要.
科学领域:
- 人工智能在医学中的应用
- 医学诊断 医学诊断 医学诊断
- 公共卫生研究 公共卫生研究
背景情况:
- 结核病 (TB) 诊断依赖于有效的方法,而耐药结核病 (DR-TB) 构成了重大挑战.
- 机器学习 (ML) 显示出结核病诊断的潜力,但对DR-TB预测和诊断的系统证据有限.
- 在医疗保健中推进人工智能 (AI) 需要对DR-TB等复杂疾病的ML工具进行强有力的评估.
研究的目的:
- 系统地审查和元分析ML模型在诊断和预测DR-TB方面的表现.
- 巩固ML对DR-TB疗效的证据,促进AI在该领域的采用.
- 为了比较诊断和预测ML模型的性能,并确定影响准确性的因素.
主要方法:
- 在主要数据库 (PubMed,Cochrane,Embase,Web of Science) 进行了系统的文献搜索,截至2025年8月,补充了谷歌学者.
- 使用PROBAST评估偏差风险,并使用双变的混合效应模型进行准确度测量的元分析.
- 进行了子组分析,以按ML任务 (诊断与预测) 和模型类型 (例如,深度学习与传统ML) 分层性能.
主要成果:
- 分析了26项涉及35,472名参与者的研究,显示诊断ML模型 (AUC0.94) 的性能优于预测模型 (AUC0.87).
- 深度学习 (DL) 诊断模型与传统的ML (AUC 0.89) 相比,实现了更高的准确性 (AUC 0.97).
- 在诊断模型 (AUC 0.95与0.85) 和预测模型 (AUC 0.88与0.85) 中,内部验证表现比外部验证更好.
结论:
- ML模型,特别是DL,对于DR-TB诊断非常有效,尽管它们的性能可能会随着外部数据集的使用而下降.
- 预测性ML模型提供中等准确度,对DR-TB的早期风险分层有价值.
- 进一步的大规模,多中心验证研究至关重要,以确认DR-TB的ML工具的稳定性和临床实用性.
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