通过深度学习模型预测氧气补充,该模型整合了COVID-19中的临床参数和胸部CT图像
Naoko Kawata1,2,3, Yuma Iwao4,5, Yukiko Matsuura6
1Department of Respirology, Graduate School of Medicine, Chiba University, 1-8-1, Inohana, Chuo-ku, Chiba-shi, Chiba, 260-8677, Japan. chumito_03@yahoo.co.jp.
Japanese journal of radiology
|July 13, 2023
概括
一个集临床数据和胸部CT扫描的深度学习模型准确地预测了COVID-19患者需要补充氧气的需求. 这种工具有助于早期诊断和资源分配,以改善患者管理.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 肺部病理学 肺部病理学
背景情况:
- 全球COVID-19病例的下降在住院护理和资源分配的早期诊断方面带来了持续的挑战.
- 预测哪些COVID-19患者需要补充氧气对于有效的医疗保健管理至关重要.
研究的目的:
- 开发和验证一种深度学习 (DL) 模型,用于预测COVID-19患者需要补充氧气的需求.
- 整合临床信息和胸部CT成像,以提高预测准确度.
主要方法:
- 使用了具有临床和胸部CT数据的738名COVID-19患者的回顾性队列.
- 一个DL模型被训练来预测氧补充需求,整合临床变量和CT发现.
- 该模型的性能在两个外部设施中得到验证.
主要成果:
- 对于预测氧气补充,DL模型实现了曲线下的面积 (AUC) 为89.9%.
- 外部验证表明,设施的AUC大于80%.
- 发现的关键预测因素包括呼吸不良和乳酸脱酶水平.
结论:
- 集成临床数据和胸部CT图像的深度学习模型在预测COVID-19患者需求方面提供了高准确度.
- 这种基于DL的方法可以显著帮助COVID-19患者的临床管理和资源配置.
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