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在COPD的COVID-19患者中,使用融合深度学习模型进行严重性评估和核酸转向负时间预测

Yanhui Liu¹, Wenxiu Zhang², Mengzhou Sun³

¹Medical Imaging Department, Hohhot First Hospital, Inner Mongolia, P.R. China.

BMC pulmonary medicine

|October 14, 2024

概括

患有肺气和慢性阻塞性肺病 (COPD) 的患者经历了更严重的COVID-19肺炎. 深度学习和放射学准确地预测这些患者的病毒清除时间.

科学领域:

肺部医学肺部医学
放射学放射学是一门学科.
人工智能的人工智能

背景情况:

慢性阻塞性肺病 (COPD) 增加了COVID-19的严重程度和肺部病变.
对于COPD患者的COVID-19预后存在有限的研究,这些患者具有不同的表型.

研究的目的:

评估COVID-19严重程度评估的深度学习和放射学.
预测COPD表型 (肺,慢性支气管炎) 的COVID-19患者的核酸转阴性时间.

主要方法:

对281名COVID-19患者 (95名COVID-19,94名肺气,92名慢性支气管炎患者) 的回顾性分析.
胸部CT扫描分析使用U-net进行肺部参与细分.
提取了107个放射学特征;使用了斯皮尔曼相关性.
深度学习和放射学融合模型开发用于预测核酸转向负时间.

主要成果:

肺气的COVID-19显示最低的淋巴细胞数量和最广泛的肺炎.
淋巴细胞数与肺部卷入和病毒清除时间相关 (r=-0.145,P<0.05).
核聚变模型在预测核酸转向负时间方面取得了80.9%的准确性.

结论:

复发性肺炎的肺表型显著恶化COVID-19肺部参与.
深度学习和放射学为预测COPDCOVID-19患者的病毒清除时间提供了有价值的工具.

关键词:

有COPD的COVID-19可能会导致COPD.深度学习方法深度学习方法核酸转向-负时间.肺部卷入肺部卷入放射学特征放射学特征放射学特征放射学特征

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