通过计算机断层扫描对COVID-19肺炎负担的人工智能辅助量化改善了对视觉评分系统的不良结果的预测
Kajetan Grodecki1,2, Aditya Killekar3, Judit Simon4,5
1Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
The British journal of radiology
|June 13, 2023
概括
人工智能 (AI) 使用胸部CT扫描比视觉评分更准确地预测COVID-19患者的临床恶化. 这种人工智能方法更快,并显示出临床分拣的潜力.
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
- 传染性疾病 传染性疾病
背景情况:
- COVID-19肺炎的诊断和预后依赖于胸部CT扫描.
- 目前在CT扫描上评估肺炎严重程度的方法通常是主观的和耗时的.
- 对肺炎负担的客观量化可能会改善患者管理.
研究的目的:
- 评估AI在量化肺炎在胸部CT扫描中的有效性.
- 将AI预测COVID-19患者临床恶化或死亡率的能力与传统视觉评分系统进行比较.
- 评估人工智能辅助量化的时间效率.
主要方法:
- 用深度学习算法进行定量性肺炎负担评估.
- 半定量性肺炎严重程度得分来自视觉评估.
- 主要结局是临床恶化 (ICU入院,机械呼吸,血管压缩剂或在医院死亡).
主要成果:
- 人工智能辅助的定量性肺炎负担显示,与视觉叶片 (0.711) 和细分 (0.722) 评分相比,用于预测临床恶化 (0.739) 的接收器操作特征曲线 (AUC) 下的区域更高.
- 人工智能量化比视觉叶片 (328s) 和细分 (698s) 评分快得多 (38s).
- 用人工智能辅助评估来计算叶膜严重性得分的性能较低 (AUC 0.723).
结论:
- 从胸部CT扫描中对肺炎负担的AI辅助量化提供了更准确的预测,对COVID-19患者的临床恶化.
- 这种AI方法比手工视觉评分方法快得多.
- 人工智能有可能在临床实践中对COVID-19患者进行基于图像的分拣.
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