基于人工智能的肺栓塞检测的元分析:深度学习模型有多可靠?
Ezio Lanza1, Angela Ammirabile1, Marco Francone1
1Humanitas University, Department of Biomedical Sciences, via Rita Levi Montalcini 4, Pieve Emanuele, 20072, Milan, Italy; IRCCS Humanitas Research Hospital, Radiology Department, via Manzoni 56, Rozzano, 20089, Milan, Italy.
Computers in biology and medicine
|May 24, 2025
概括
深度学习模型在CT肺血管造影 (CTPA) 上检测肺栓塞 (PE) 的高精度. 虽然U-Net架构提高了灵敏度,而CNN则提高了特异性,但临床使用需要进一步验证.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 深度学习 (DL) 在CT肺血管造影 (CTPA) 上检测肺栓塞 (PE) 方面表现有前途.
- DL方法可以提高PE检测的诊断准确性和工作流程效率.
- 这一元分析评估了DL算法对PE检测的性能.
研究的目的:
- 确定用于PE检测的DL算法的聚合性能估计.
- 将卷积神经网络 (CNN) 和U-Net架构之间用于PE检测的诊断效率进行比较.
主要方法:
- 按照PRISMA指南,对PubMed和EMBASE (2010-2025) 的系统文献搜索.
- 聚合的绩效指标 (灵敏度,特异性,AUROC等) 使用随机效应模型.
- 分组分析比较CNN与U-Net模型用于PE检测.
主要成果:
- 包括24项研究 (22,984名患者);高的综合诊断准确性 (AUROC 0.895).
- U-Net模型显示出更高的灵敏度 (0.899) 和CNN模型显示出更高的特异性 (0.926).
- 观察到显著的异质性 (I2 ≈ 97%),表明跨研究的可变性.
结论:
- DL算法在CTPA上检测PE的诊断准确度很高.
- U-Net在真正阳性识别方面表现出色,而CNN则减少了假阳性.
- 由于异质性,在常规临床实施之前,标准化前性验证至关重要.
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