Background and Aims. Chest X-ray (CXR) is indispensable to the assessment of severity, diagnosis, and management of pneumonia. Deep learning is an artificial intelligence (AI) technology that has been applied to the interpretation of medical images. This study investigated the feasibility of classifying fatal pneumonia based on CXR images using deep learning models on publicly available platforms. Methods. CXR images of patients with pneumonia at diagnosis were labeled as fatal or nonfatal based on medical records. We applied CXR images from 1031 patients with nonfatal pneumonia and 243 patients with fatal pneumonia for training and self-evaluation of the deep learning models. All labeled CXR images were randomly allocated to the training, validation, and test datasets of deep learning models. Data augmentation techniques were not used in this study. We created two deep learning models using two publicly available platforms. Results. The first model showed an area under the precision-recall curve of 0.929 with a sensitivity of 50.0% and a specificity of 92.4% for classifying fatal pneumonia. We evaluated the performance of our deep learning models using sensitivity, specificity, PPV, negative predictive value (NPV), accuracy, and F1 score. Using the external validation test dataset of 100 CXR images, the sensitivity, specificity, accuracy, and F1 score were 68.0%, 86.0%, 77.0%, and 74.7%, respectively. In the original dataset, the performance of the second model showed a sensitivity, specificity, and accuracy of 39.6%, 92.8%, and 82.7%, respectively, while external validation showed values of 38.0%, 92.0%, and 65.0%, respectively. The F1 score was 52.1%. These results were comparable to those obtained by respiratory physicians and residents. Conclusions. The deep learning models yielded good accuracy in classifying fatal pneumonia. By further improving the performance, AI could assist physicians in the severity assessment of patients with pneumonia.

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使用胸部 X 射线图像预测致命性肺炎的深度学习模型

背景和目标。胸部 X 光检查 (CXR) 对于评估肺炎的严重程度、诊断和管理是必不可少的。深度学习是一种人工智能 (AI) 技术，已应用于医学图像的解读。本研究调查了在公共平台上使用深度学习模型根据 CXR 图像对致命性肺炎进行分类的可行性。方法. 根据医疗记录，诊断为肺炎患者的 CXR 图像被标记为致命或非致命。我们应用了 1031 名非致命性肺炎患者和 243 名致命性肺炎患者的 CXR 图像来训练和自我评估深度学习模型。所有标记的 CXR 图像被随机分配到深度学习模型的训练、验证和测试数据集。本研究未使用数据增强技术。我们使用两个公开可用的平台创建了两个深度学习模型。结果. 第一个模型显示精确回忆曲线下的面积为 0.929，对致命性肺炎进行分类的灵敏度为 50.0%，特异性为 92.4%。我们使用灵敏度、特异性、PPV、阴性预测值 (NPV)、准确性和 F1 分数评估了我们的深度学习模型的性能。使用 100 张 CXR 图像的外部验证测试数据集，灵敏度、特异性、准确性和 F1 分数分别为 68.0%、86.0%、77.0% 和 74.7%。在原始数据集中，第二个模型的性能分别显示 39.6%、92.8% 和 82.7% 的灵敏度、特异性和准确性，而外部验证显示的值分别为 38.0%、92.0% 和 65.0%。F1得分为52.1%。这些结果与呼吸内科医师和住院医师获得的结果相当。结论。深度学习模型在对致命性肺炎进行分类方面取得了良好的准确性。通过进一步提高性能，人工智能可以帮助医生评估肺炎患者的严重程度。