基于人工智能的算法的精度在肺栓塞检测计算机断层肺血管学:一个更新的系统审查和元分析
Seyed Ali Nabipoorashrafi1, Arsalan Seyedi2, Razman Arabzadeh Bahri3
1Cardiothoracic Imaging Section, Department of Radiology, University of Washington, 1959 NE Pacific Street Room RR215F, Box 357115, Seattle, WA, 98195, USA.
Journal of imaging informatics in medicine
|September 15, 2025
概括
人工智能 (AI) 算法在使用计算机断层肺血管学 (CTPA) 检测肺栓塞 (PE) 中显示出高准确度. 这一元分析证实了AI的存在.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 放射学 放射学是一门学科.
背景情况:
- 肺栓塞 (PE) 诊断依赖于计算机断层肺血管造影 (CTPA).
- 许多人工智能 (AI) 算法已被开发用于使用CTPA检测PE.
- 需要更新的元分析来评估这些AI算法的诊断准确性.
研究的目的:
- 系统地审查和元分析基于AI的算法通过CTPA诊断PE的准确性.
- 为人工智能在PE检测方面的表现提供最新评估.
主要方法:
- 在EMBASE,PubMed,Web of Science和Cochrane数据库中进行系统的文献搜索.
- 包括报告AI算法在CTPA上检测PE的敏感性和特异性的研究.
- 使用R软件进行单变量和双变量元分析,包括用于探索异质性的子组分析.
主要成果:
- 使用26个人工智能模型的20项研究,包括11,950名参与者,符合纳入标准.
- 在基于AI的PE检测中,聚合灵敏度为91.5%,聚合特异性为84.3%.
- 接收器操作特征曲线 (AUC) 下的聚合面积为0.923,表明诊断准确度非常高. 地理区域被确定为异质性的来源.
结论:
- 人工智能算法在使用CTPA诊断肺栓塞方面表现出有希望和高准确性.
- 需要进一步的研究来解决区域差异,并提高PE检测人工智能模型的通用性.
- 人工智能具有很大的潜力,可以改善PE的诊断工作流程.
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