通过超声波图像对急性腹腔大动脉剖析进行深度学习辅助的诊断
Zhanye Lin1, Jian Zheng1, Yaohong Deng2
1Ultrasound Department of The Second Affiliated Hospital, School of Medicine, The Chinese University of Hong Kong, Shenzhen & Longgang District People's Hospital of Shenzhen, Shenzhen, China.
Emergency radiology
|January 17, 2025
概括
深度学习 (DL) 使用超声波 (US) 图像有效地诊断急性腹腔大动脉解剖 (AD),表现优于人类读者. 这个由人工智能驱动的工具可以帮助紧急诊断.
科学领域:
- 医学成像分析分析 医学成像分析
- 医疗保健中的人工智能
- 心血管诊断心血管诊断
背景情况:
- 急性腹腔大动脉解剖 (AD) 是一种需要快速诊断的危急状况.
- 通过超声波 (US) 早期检测可以显著改善患者的预后,特别是在紧急情况下.
- 新手放射科医生和非专业人士可以从AD的诊断支持工具中受益.
研究的目的:
- 评估深度学习 (DL) 算法在从美国图像中诊断腹部AD的能力.
- 评估DL的潜力,以帮助经验较少的人员诊断AD.
- 将DL模型的诊断性能与人类阅读器进行比较.
主要方法:
- 利用了374张美国图像,分类为AD阳性或AD阴性.
- 实现了Densenet-169和VGG-16深度学习模型.
- 将DL模型的性能与两个人类阅读器进行比较,使用90%/10%的训练/测试分割.
主要成果:
- 在诊断腹部AD时,DL模型实现了高灵敏度和曲线下的面积 (AUC).
- DL模型的诊断性能通常超过了人类读者.
- 通过DL分析的美国图像显示了AD的显著诊断准确性.
结论:
- 深度学习通过美国成像显示了帮助诊断腹部AD的有效性.
- 基于DL的诊断工具有望改善紧急情况下的AD检测.
- 人工智能辅助诊断可以提高AD识别的准确性和速度.
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