基于深度学习的动脉狭窄的诊断通过多式融合
Xin Wang1, Sheng Cai1, Hongyan Wang1
1Department of Ultrasound, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.
Journal of applied clinical medical physics
|February 19, 2024
概括
这项研究开发了一种深度学习模型,用于使用多式数据诊断动脉狭窄 (RAS). 该模型在协助临床医生进行RAS诊断方面表现有前途.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 心血管诊断心血管诊断服务
背景情况:
- 诊断动脉狭窄 (RAS) 是一个挑战.
- 多模式聚变技术提供了一个潜在的解决方案.
研究的目的:
- 开发一个深度学习 (DL) 模型用于RAS的计算机辅助诊断.
- 利用超声图像,光谱波形和临床数据的多式融合.
主要方法:
- 在一个多式联网数据集上训练了三个DL模型 (ResNeSt,ResNet,XCiT).
- 数据集包括彩色多普勒超声波图像,频谱波形图像和患者信息.
- 在测试组 (N=117) 上评估模型,动脉血管学结果.
主要成果:
- DL模型在测试数据上实现了超过80%的准确性.
- 在ResNeSt模型中,准确度为83.49%,精度为81.89%,回忆率为76.97%.
- 多模式融合模型的性能优于单模式模型,特别是在频谱波形数据上.
结论:
- 深度学习多式联络融合模型显示了协助RAS诊断的潜力.
- 进一步开发可以提高诊断的准确性和效率.
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