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Association of AI quantified COVID-19 chest CT and patient outcome.

AI量化新型冠状病毒肺炎胸部CT与患者预后的相关性。

  • 影响因子:2.34
  • DOI:10.1007/s11548-020-02299-5
  • 作者列表:"Fang X","Kruger U","Homayounieh F","Chao H","Zhang J","Digumarthy SR","Arru CD","Kalra MK","Yan P
  • 发表时间:2021-03-01
Abstract

PURPOSE:Severity scoring is a key step in managing patients with COVID-19 pneumonia. However, manual quantitative analysis by radiologists is a time-consuming task, while qualitative evaluation may be fast but highly subjective. This study aims to develop artificial intelligence (AI)-based methods to quantify disease severity and predict COVID-19 patient outcome. METHODS:We develop an AI-based framework that employs deep neural networks to efficiently segment lung lobes and pulmonary opacities. The volume ratio of pulmonary opacities inside each lung lobe gives the severity scores of the lobes, which are then used to predict ICU admission and mortality with three different machine learning methods. The developed methods were evaluated on datasets from two hospitals (site A: Firoozgar Hospital, Iran, 105 patients; site B: Massachusetts General Hospital, USA, 88 patients). RESULTS:AI-based severity scores are strongly associated with those evaluated by radiologists (Spearman's rank correlation 0.837, [Formula: see text]). Using AI-based scores produced significantly higher ([Formula: see text]) area under the ROC curve (AUC) values. The developed AI method achieved the best performance of AUC = 0.813 (95% CI [0.729, 0.886]) in predicting ICU admission and AUC = 0.741 (95% CI [0.640, 0.837]) in mortality estimation on the two datasets. CONCLUSIONS:Accurate severity scores can be obtained using the developed AI methods over chest CT images. The computed severity scores achieved better performance than radiologists in predicting COVID-19 patient outcome by consistently quantifying image features. Such developed techniques of severity assessment may be extended to other lung diseases beyond the current pandemic.

摘要

目的: 严重程度评分是管理新型冠状病毒肺炎肺炎患者的关键步骤。然而,放射科医师的人工定量分析是一项耗时的任务,而定性评估可能是快速但高度主观的。本研究旨在开发基于人工智能 (AI) 的方法来量化疾病严重程度并预测新型冠状病毒肺炎患者结果。 方法: 我们开发了一个基于AI的框架,利用深度神经网络有效地分割肺叶和肺部阴影。每个肺叶内肺部混浊的体积比给出肺叶的严重程度评分,然后用三种不同的机器学习方法预测ICU入院和死亡率。在来自两家医院的数据集上评估所开发的方法 (站点A: 伊朗Firoozgar医院,105名患者; 站点B: 美国马萨诸塞州总医院,88名患者)。 结果: 基于AI的严重程度评分与放射科医师评估的严重程度评分密切相关 (Spearman's等级相关性0.837,[公式: 见正文])。使用基于AI的评分产生显著更高的 ([公式: 参见文本]) ROC曲线下面积 (AUC) 值。开发的AI方法在预测ICU入院方面达到了AUC = 0.813 (95% CI [0.729,0.886]) 和AUC = 0.741 (95% CI [0.640,0.837]) 在两个数据集上的死亡率估计中的最佳性能。 结论: 使用开发的AI方法在胸部CT图像上可以获得准确的严重程度评分。通过一致地量化图像特征,计算的严重程度评分在预测新型冠状病毒肺炎患者结果方面实现了比放射科医生更好的性能。这种开发的严重性评估技术可以扩展到当前大流行之外的其他肺部疾病。

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影响因子:2.68
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