随机急性肺栓塞病例的定量评估和风险分层使用基于计算机断层扫描肺血管图像的深度学习模型
Yang Qiao1,2, Yaozong Gao3, Yanbo Chen3
1Department of Radiology, Zhongshan Hospital Affiliated to Fudan University, Shanghai, China.
Quantitative imaging in medicine and surgery
|March 31, 2025
概括
一个新的深度学习模型,VB-Net,准确地检测肺栓塞 (PE),并从CTPA扫描中量化血栓负担. 这种自动化方法有助于早期诊断和风险分层,以改善患者的治疗结果.
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:
- 计算机断层扫描肺血管造影 (CTPA) 是诊断肺栓塞 (PE) 的标准.
- 半定量凝块负担评分有助于PE风险分层,但在临床上应用具有挑战性.
- 目前用于评估凝块负担的方法对于放射科医生来说是困难和耗时的.
研究的目的:
- 开发一个VB-Net深度学习 (DL) 模型与变压器相结合,用于自动化PE检测和凝块负担评分.
- 帮助早期诊断,风险分层和PE患者的治疗计划.
- 为了减少放射科医生在分析CTPA扫描中的工作量.
主要方法:
- 追溯分析了2,424个CTPA案例,以训练和测试VB-Net DL模型.
- 用曲线下的面积 (AUC),病例和凝块水平的灵敏度和特异性来评估模型性能.
- 在独立数据上对手动确定的Qanadli分数和其他成像参数进行验证.
主要成果:
- 在测试组 (病例级) 上,VB-Net模型实现了0.972的AUC.
- 在选择的模型值观察到高灵敏度 (94.6%) 和特异性 (89.4%).
- 自动化凝血负担测量与手动的Qanadli分数有很强的相关性 (r=0.866,P<0.001).
结论:
- VB-Net DL模型有效地使用CTPA检测肺栓塞.
- 该模型提供了方便和高效的PE凝块负担的定量评估.
- 这种自动化系统显示了改善临床工作流程和患者管理的潜力.
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