利用胸部 X 光图像中的深度学习技术确定 COVID-19 患者的疾病严重程度。
Determination of disease severity in COVID-19 patients using deep learning in chest X-ray images.
机构信息
Center for Interventional Oncology, National Institutes of Health Clinical Center and National Cancer Institute, Bethesda, Maryland, USA.
Philips Research North America, Cambridge, Massachusetts, USA.
出版信息
Diagn Interv Radiol. 2021 Jan;27(1):20-27. doi: 10.5152/dir.2020.20205.
PURPOSE
Chest X-ray plays a key role in diagnosis and management of COVID-19 patients and imaging features associated with clinical elements may assist with the development or validation of automated image analysis tools. We aimed to identify associations between clinical and radiographic features as well as to assess the feasibility of deep learning applied to chest X-rays in the setting of an acute COVID-19 outbreak.
METHODS
A retrospective study of X-rays, clinical, and laboratory data was performed from 48 SARS-CoV-2 RT-PCR positive patients (age 60±17 years, 15 women) between February 22 and March 6, 2020 from a tertiary care hospital in Milan, Italy. Sixty-five chest X-rays were reviewed by two radiologists for alveolar and interstitial opacities and classified by severity on a scale from 0 to 3. Clinical factors (age, symptoms, comorbidities) were investigated for association with opacity severity and also with placement of central line or endotracheal tube. Deep learning models were then trained for two tasks: lung segmentation and opacity detection. Imaging characteristics were compared to clinical datapoints using the unpaired student's t-test or Mann-Whitney U test. Cohen's kappa analysis was used to evaluate the concordance of deep learning to conventional radiologist interpretation.
RESULTS
Fifty-six percent of patients presented with alveolar opacities, 73% had interstitial opacities, and 23% had normal X-rays. The presence of alveolar or interstitial opacities was statistically correlated with age (P = 0.008) and comorbidities (P = 0.005). The extent of alveolar or interstitial opacities on baseline X-ray was significantly associated with the presence of endotracheal tube (P = 0.0008 and P = 0.049) or central line (P = 0.003 and P = 0.007). In comparison to human interpretation, the deep learning model achieved a kappa concordance of 0.51 for alveolar opacities and 0.71 for interstitial opacities.
CONCLUSION
Chest X-ray analysis in an acute COVID-19 outbreak showed that the severity of opacities was associated with advanced age, comorbidities, as well as acuity of care. Artificial intelligence tools based upon deep learning of COVID-19 chest X-rays are feasible in the acute outbreak setting.
目的
胸部 X 光在 COVID-19 患者的诊断和治疗中起着关键作用，与临床元素相关的影像学特征可能有助于开发或验证自动化图像分析工具。我们旨在确定临床和影像学特征之间的关联，并评估深度学习在急性 COVID-19 爆发期间应用于胸部 X 光的可行性。
方法
对 2020 年 2 月 22 日至 3 月 6 日期间，意大利米兰一家三级护理医院的 48 例 SARS-CoV-2 RT-PCR 阳性患者（年龄 60±17 岁，女性 15 例）的 X 光、临床和实验室数据进行了回顾性研究。两名放射科医生对 65 张胸部 X 光片进行了肺泡和间质性混浊的回顾，并按严重程度 0-3 级进行分类。研究了临床因素（年龄、症状、合并症）与混浊严重程度的关系，以及与中央线或气管内管的关系。然后，为两项任务训练深度学习模型：肺分割和混浊检测。使用未配对的学生 t 检验或曼惠特尼 U 检验比较影像学特征和临床数据点。使用 Cohen's kappa 分析评估深度学习与传统放射科医生解释的一致性。
结果
56%的患者表现为肺泡混浊，73%的患者有间质性混浊，23%的患者 X 光正常。肺泡或间质性混浊的存在与年龄（P=0.008）和合并症（P=0.005）呈统计学相关。基线 X 光片上肺泡或间质性混浊的严重程度与气管内管（P=0.0008 和 P=0.049）或中央线（P=0.003 和 P=0.007）的存在显著相关。与人类解释相比，深度学习模型对肺泡混浊的kappa 一致性为 0.51，对间质性混浊的kappa 一致性为 0.71。
结论
在急性 COVID-19 爆发期间的胸部 X 光分析表明，混浊的严重程度与年龄较大、合并症以及治疗的紧迫性有关。基于 COVID-19 胸部 X 光深度学习的人工智能工具在急性爆发环境中是可行的。
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