基于深度学习的诊断 Femoropopliteal动脉使用最大强度投影图像的CT血管学Steno-Occlusion
Wonju Hong1, Jaewoong Kang2,3, So Eui Kim2
1Department of Radiology, Hallym University Sacred Heart Hospital, College of Medicine, Hallym University, Anyang 14068, Republic of Korea.
Tomography (Ann Arbor, Mich.)
|September 26, 2025
概括
深度学习模型通过CT血管学有效地检测外围动脉中显著的狭窄阻塞 (SSO). 在最大强度投影图像上,RDNet在识别>50%的缩小方面表现出色.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 心血管成像 - 心血管成像
背景情况:
- 股骨关节动脉中显著的狭窄封闭 (SSO) 会影响患者的健康.
- 准确检测SSO对于及时干预至关重要.
- 当前的检测方法可能会从先进的计算方法中受益.
研究的目的:
- 开发和验证深度学习模型,以检测股骨关节动脉中显著的狭窄阻塞 (SSO).
- 评估模型性能使用从下肢CT血管造影 (CTA) 的最大强度投影 (MIP) 图像.
- 为了比较不同的深度学习架构和SSO检测的输入策略.
主要方法:
- 从2021年1月到2023年12月的下肢CTA MIP图像进行回顾性分析,用于内部模型开发.
- 深度学习模型的顺序开发:单个前后影像选,然后用多角度图像进行四段旋转分析.
- 培训模型用于分类SSO存在,具有从1月到2024年6月的时间验证数据集.
主要成果:
- 在SSO检测的单图像分析中,RDNet实现了0.886的最高平均曲线下面面积 (AUC).
- 在四段旋转分析中,RDNet显示最高的平均AUC为0.964.
- 在时间验证数据集中,RDNet保持了出色的表现,平均AUC为0.959.
结论:
- 深度学习模型,特别是RDNet,在检测下肢CTA MIP图像上的外周动脉SSO方面表现出色.
- 与单图像查相比,四段旋转分析方法提高了检测准确性.
- 这些发现支持人工智能驱动的工具的潜力,以改善股骨关节动脉狭窄的诊断.
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