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Updated: Jan 13, 2026

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

预测COVID-19肺炎的短期结果使用基于深度学习的自动检测算法分析连续胸部X射线图的分析.

Chae Young Lim¹, Yoon Ki Cha¹, Kyeongman Jeon^2,3

¹Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro Gangnam-gu, Seoul 06351, Republic of Korea.

Bioengineering (Basel, Switzerland)

|October 29, 2025

概括

分析胸部放射图的深度学习算法可以预测COVID-19肺炎的短期结果. 基于深度学习的自动检测算法 (DLAD) 的参数变化显示了患者改善或恶化的预后价值.

关键词:

在 COVID-19 疫情中,这是Grad-CAM.胸部X射线扫描胸部X射线扫描商业人工智能商业人工智能时间依赖的接收器操作特征曲线.

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科学领域:

放射学放射学是一门学科.
人工智能在医学中的应用
传染性疾病传染性疾病

背景情况:

COVID-19肺炎是一个重大的临床挑战,需要准确的结果预测.
连续的胸部放射 (CXR) 对于监测疾病进展至关重要.
深度学习算法为医疗成像的自动化分析提供了潜力.

研究的目的:

评估基于深度学习的自动检测算法 (DLAD) 在预测COVID-19肺炎患者短期临床结果方面的有效性.
为了评估从DLAD推导的参数的预后值,应用于连续CXRs.
识别成像生物标志物,以预测患者的改善或恶化.

主要方法:

来自391名COVID-19肺炎患者的连续CXR的分析.
应用DLAD以使用热图细分来量化整合概率和面积.
在成像参数中计算权重面积和变化率 (Δ).
开发和评估可克斯比例危险回归模型,用于每日结果预测.

主要成果:

从DLAD获得的基线概率和Δ参数 (Δ概率, Δ面积, Δ加权面积) 是显著的预后指标.
使用基线概率和Δ权重区域的多变量考克斯模型实现了最佳预测性能 (C指数:0.75).
时间依赖的AUROC值从0.74到0.78不等,表明可靠的每日预测准确度.

结论:

DLAD参数,特别是随时间的变化 (Δ参数),可以有效预测COVID-19肺炎的短期临床结果.
这种使用序列CXR的AI驱动方法显示出对患病恶化风险的患者早期鉴定有前途.
这些发现支持将DLAD整合到临床工作流程中,以改善患者管理.