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Updated: Jun 25, 2025

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

对比视觉和基于软件的量化评估评分,对COVID-19患者的肺细胞参与量化

Marco Nicolò¹, Altin Adraman², Camilla Risoli³

¹Department of Diagnostic Imaging, Spedali Civili di Brescia, Piazzale Spedali Civili 1, 25123 Brescia, Italy.

Diagnostics (Basel, Switzerland)

|May 24, 2024

概括

这项研究比较了COVID-19肺部评估的视觉和基于AI的CT扫描得分. 视觉评估显示了良好的协议,而AI软件显示了适度的协议,这表明AI工具可以帮助诊断.

科学领域:

放射学和医学成像学医学成像学
人工智能在医学中的应用
传染病诊断传染病诊断传染病诊断

背景情况:

计算机断层扫描 (CT) 对于COVID-19的特征和随访至关重要.
视觉定量评估得分 (VQAS) 和基于软件的定量评估得分 (SBQAS) 有助于管理SARS-CoV-2感染.
将VQAS和SBQAS的诊断精度与AI软件进行比较是必不可少的.

研究的目的:

调查和比较VQAS和SBQAS的诊断准确性.
评估两个不同的人工智能 (AI) 驱动的软件程序 (Icolung和CT-COPD).
评估人工智能工具与医生专业知识一起用于COVID-19患者管理.

主要方法:

对90名COVID-19阳性患者 (年龄大于18岁) 的回顾性研究,其胸部CT扫描没有增强.
使用Icolung和CT-COPD软件对VQAS和SBQAS进行独立评估.
使用类内相关系数 (ICC) 和布兰德-阿尔特曼图表进行统计分析.

主要成果:

对于VQAS,有良好的放射科医生间协议 (ICC = 0.871).
对于SBQAS的两个AI软件类型之间的中等协议 (ICC = 0.584).
在IColung和中位数VQAS之间达成良好协议 (ICC = 0.885);对CT-COPD的中等协议 (ICC = 0.622).

结论:

VQAS和SBQAS显示中等到良好的协议,证明对COVID-19患者管理有价值.
人工智能工具在协助SARS-CoV-2的放射性评估方面显示出潜力.
将人工智能工具与医生的专业知识相结合,可以优化COVID-19患者的诊断和治疗计划.

关键词:

在 COVID-19 疫情中,人工智能的人工智能是人工智能.胸部CT CT 胸部CT CT 胸部CT 胸部CT 深度学习是一种深度学习.基于软件的得分计算软件.视觉评分视觉评分视觉评分

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