基于人工智能的胸部计算机断层扫描和临床/实验室特征与COVID-19住院患者的严重程度和死亡率之间的关联
Jiawei Ye1, Yingying Huang2, Caiting Chu3
1Department of Emergency Medicine, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, People's Republic of China.
Journal of inflammation research
|May 20, 2024
概括
胸部CT扫描上的AI量化肺病变,以及临床因素,可以预测严重的COVID-19疾病和死亡率. 这种方法有助于早期识别高风险患者,改善临床结果.
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:
- COVID-19患者可以迅速发展严重的呼吸衰竭和死亡率.
- 早期识别患有严重疾病风险的患者至关重要.
- 很少有研究探讨了胸部计算机断层扫描 (CT) 对预测COVID-19严重性的有用性.
研究的目的:
- 通过早期CT扫描,以数值量化肺病变.
- 评估人工智能 (AI) 算法的作用与临床和实验室数据相结合.
- 帮助临床医生早期识别COVID-19患者的严重疾病和死亡.
主要方法:
- 191名COVID-19患者在入院48小时内接受了胸部CT扫描和血清测试.
- 使用多变量逻辑回归分析了与危急疾病或死亡率显著相关的变量.
- 预测性能的评估是使用几率比率,置信区间,灵敏度,特异性和接收者-操作者特征 (ROC) 曲线.
主要成果:
- 肌红蛋白,APACHE II评分和感染CT区域的百分比独立与住院死亡率相关.
- 准专辑蛋白被确定为一种独立的预防死亡的保护因素.
- 中性粒细胞计数,尿素,SOFA得分,qSOFA得分,PSI得分和感染CT区域的百分比与COVID-19患者的严重程度有关.
结论:
- 在CT扫描上对肺病变的AI驱动量化是预测COVID-19严重程度和死亡率的有希望的工具.
- 将CT成像与临床和实验室标记器相结合,可以提高早期风险分层.
- 这种方法可以支持临床决策,以便在高风险的COVID-19患者中及时干预.
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