基于深度学习的非对比CT用于检测急性缺血性中风:对患者水平诊断准确性的系统审查和HSROC元分析
Kalab Yigermal Gete1,2, Asnakew Achaw Ayele3,4
1School of Medicine, College of Medicine and Health Sciences, Bahir Dar University, Bahir Dar, Ethiopia. kalabgete@gmail.com.
BMC neurology
|December 15, 2025
概括
深度学习 (DL) 在非对比CT扫描上显著提高了急性缺血性中风 (AIS) 检测. 虽然准确,但进一步的外部验证对于广泛的临床使用至关重要.
科学领域:
- 放射学和医学成像学 医学成像学
- 医疗保健中的人工智能
- 神经学 神经学
背景情况:
- 非对比CT (NCCT) 是疑似急性缺血性中风 (AIS) 的主要成像模式.
- 在检测AIS时,NCCT的早期灵敏度有限.
- 深度学习 (DL) 算法显示出改善NCCT上患者级AIS检测的潜力.
研究的目的:
- 为了评估DL的诊断准确性,它应用于NCCT,以确定患者层面的AIS.
- 调查导致在研究中对AIS检测的DL进行评估的研究异质性的因素.
主要方法:
- 在MEDLINE,Embase和Web of Science的系统文献搜索 (2010年1月至2025年5月).
- 在NCCT上包括使用DL的诊断研究,以及患者级数据和适当的参考标准.
- 使用双变随机效应/HSROC模型进行元分析;使用QUADAS-2评估偏差风险;与STARD-AI/CLAIM/CONSORT-AI调整对比评估AI报告.
主要成果:
- 包括16项研究,其中13项为分析做出了贡献.
- 总体总结灵敏度为0.91 (95% CI,0.81-0.96) 和特异性为0.90 (0.85-0.94).
- 与内部验证的模型 (0.95) 相比,外部验证的模型显示出较低的灵敏度 (0.82),具有类似的特异性.
结论:
- 应用于NCCT的DL显示了患者级AIS检测的高准确性.
- 通用性仍然是一个关键问题,需要更广泛的外部验证.
- 改善报告和遵守指南对于安全的临床整合DL在AIS检测至关重要.
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