使用深度学习对CT扫描图像进行定量评估,以确定COVID-19患者的预后
Saeid Sadeghi Joni1, Reza Gerami2, Fakhereh Pashaei3
1Department of Radiology, Faculty of medicine, Aja University of Medical Sciences, Tehran. saeedsadeghi69@gmail.com.
European journal of translational myology
|July 26, 2023
概括
在CT扫描上人工智能辅助的肺结合量化准确地预测了COVID-19的进展和临床结果. 这种人工智能工具有助于对COVID-19患者的风险分层和预后评估.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 肺部医学 肺部医学
背景情况:
- COVID-19的诊断和严重程度评估依赖于肺部CT扫描.
- 准确预测疾病进展和临床结果对于患者管理至关重要.
- 传统的CT分析包括视觉评估和半定量评分,这可能是主观的.
研究的目的:
- 将人工智能辅助的肺病变量化与传统CT参数进行比较,以预测COVID-19的严重程度,进展和临床结果.
- 评估AI在COVID-19患者风险分层和预后评估中的实用性.
主要方法:
- 对291名接受肺部CT扫描的COVID-19患者进行横截面研究.
- 最初的CT扫描被分析为磨砂玻璃不透明度 (GGO) 和整合.
- 基于人工智能对GGO和整合量的量化与传统的半量化CT严重性评分一起进行.
主要成果:
- 巩固体积百分比是疾病进展的最强预测指标 (AUC = 0.91,p < 0.001).
- 与不严重的组相比,疾病进展的患者显示出明显更高的GGO和整合体积.
- 较大的整合量与较差的临床结果相关.
结论:
- 在初始胸部CT上,人工智能辅助的整合体积量化是COVID-19进展的高度准确的预测指标.
- 基于AI的病变量化对于COVID-19患者的风险分层和预后评估非常有价值.
- 巩固体积是预测疾病严重程度和临床结果的关键成像生物标志物.
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