深度学习用于在后续设置中自动选稳定的胸部X射线图
Jihye Yun1, Yura Ahn1, Kyungjin Cho1
1From the Department of Radiology and Research Institute of Radiology (J.Y., Y.A., S.Y.O., S.M.L., J.B.S.) and Department of Convergence Medicine (K.C., N.K.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 138-736, Korea.
Radiology
|October 24, 2023
概括
一个新的深度学习算法有效地分类胸部X光学对,识别在纵向随访期间没有显著变化的病例. 这种人工智能工具有助于管理重症监护成像,通过在急诊室和重症监护室标记紧急间隔发现.
科学领域:
- 医疗成像中的人工智能
- 对于放射分析的深度学习.
- 胸部成像解释的解释
背景情况:
- 目前用于胸部放射图的AI通常分析单个时间点.
- 临床实践,特别是ICU中,依赖于连续射线图来监测患者.
- 需要人工智能来解释胸部X射线的纵向变化.
研究的目的:
- 开发和验证深度学习算法,用于对胸部X光学对进行分类.
- 算法使用胸腔注册和减去进行纵向数据分析.
- 目标是识别没有显著变化的对,以促进有效的后续行动.
主要方法:
- 使用成年人胸部放射图 (2011-2018) 进行深度学习算法的回顾性开发.
- 两个胸部放射科医生为"变化"和"不变化"图像对建立了基本真理.
- 使用AUC分析对验证和内部测试集 (ED和ICU) 进行值校准的性能评估.
主要成果:
- 算法实现了0.77 (验证),0.80 (ED) 和0.80 (ICU) 的AUC.
- 在40%的分拣值下,特异性为88.4% (ED) 和90.0% (ICU).
- 高特异性 (78.6%-100%ED,85.5%-93.9%ICU) 紧急发现在40%的门.
结论:
- 开发的深度学习算法成功地分类了胸部X射线图对,显示没有间隔变化.
- 该算法还可以在纵向跟踪期间检测紧急间隔变化.
- 提高工作流效率的潜力,分析在重症监护机构的连续胸部X射线.
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