通过深度学习实现的超低剂量X射线图像检测COVID-19
Isah Salim Ahmad1, Na Li2, Tangsheng Wang1
1Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
Bioengineering (Basel, Switzerland)
|November 25, 2023
概括
一个新的深度神经网络ULTRA-X-COVID,使用超低剂量X射线图像准确检测2019年新冠病毒病 (COVID-19). 这种人工智能模型提供了一个安全而快速的替代方案,用于COVID-19的诊断,辐射暴露最小.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 传染病诊断 传染病诊断 传染病诊断
背景情况:
- 用于COVID-19诊断的常规X射线成像涉及显著的辐射暴露,限制了重复检查.
- 超低剂量X射线技术为快速和准确的疾病检测提供了更安全的替代方案.
- 人工智能 (AI) 越来越多地用于医学图像分析和疾病诊断.
研究的目的:
- 引入和评估ULTRA-X-COVID,这是一个用于使用超低剂量X射线图像自动检测COVID-19的深度神经网络.
- 在大型,跨国和多中心数据集上评估ULTRA-X-COVID模型的性能.
- 将超低剂量X射线成像与人工智能的诊断能力与传统X射线方法进行比较.
主要方法:
- 一项回顾性队列研究 (ULTRA-X-COVID) 使用来自51个国家的约16,600名患者的30,882张超低剂量X射线图像进行.
- 为了自动检测COVID-19,开发了一个深度神经网络 (ULTRA-X-COVID),具有不同的训练和测试数据集.
- 模型性能使用包括AUC,精度,特异性和F1分数在内的指标进行评估.
主要成果:
- 在ULTRA-X-COVID模型中,AUC为0.968,准确度为94.3%,特异性为88.9%,F1得分为99.0%.
- 该模型的性能与传统的X射线剂量相当.
- 每张图像的预测时间很快,平均只有0.1秒.
结论:
- ULTRA-X-COVID模型有效地通过超低剂量X射线扫描识别COVID-19感染.
- 这种由人工智能驱动的方法为COVID-19检测提供了一种新,安全和高效的替代方案.
- ULTRA-X-COVID模型显示了通过医学成像来诊断其他疾病的适应潜力.
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