Objectives There exists a wide gap in the availability of mechanical ventilator devices and their acute need in the context of the COVID-19 pandemic. An initial triaging method that accurately identifies the need for mechanical ventilation in hospitalised patients with COVID-19 is needed. We aimed to investigate if a potentially deteriorating clinical course in hospitalised patients with COVID-19 can be detected using all X-ray images taken during hospitalisation. Methods We exploited the well-established DenseNet121 deep learning architecture for this purpose on 663 X-ray images acquired from 528 hospitalised patients with COVID-19. Two Pulmonary and Critical Care experts blindly and independently evaluated the same X-ray images for the purpose of validation. Results We found that our deep learning model predicted the need for mechanical ventilation with a high accuracy, sensitivity and specificity (90.06%, 86.34% and 84.38%, respectively). This prediction was done approximately 3 days ahead of the actual intubation event. Our model also outperformed two Pulmonary and Critical Care experts who evaluated the same X-ray images and provided an incremental accuracy of 7.24%–13.25%. Conclusions Our deep learning model accurately predicted the need for mechanical ventilation early during hospitalisation of patients with COVID-19. Until effective preventive or treatment measures become widely available for patients with COVID-19, prognostic stratification as provided by our model is likely to be highly valuable. Data and code are available from the author on reasonable request.

中文翻译：

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深度学习模型利用胸部 X 光图像预测住院 COVID-19 患者是否需要机械通气

目标 在 COVID-19 大流行的背景下，机械呼吸机设备的可用性及其迫切需求存在巨大差距。需要一种初步分类方法来准确确定住院的 COVID-19 患者是否需要机械通气。我们的目的是调查是否可以使用住院期间拍摄的所有 X 射线图像来检测住院的 COVID-19 患者潜在恶化的临床病程。方法 为此，我们利用了完善的 DenseNet121 深度学习架构，对 528 名住院的 COVID-19 患者采集的 663 张 X 射线图像进行了分析。两名肺部和重症监护专家盲目且独立地评估了相同的 X 射线图像以进行验证。结果我们发现，我们的深度学习模型以较高的准确度、灵敏度和特异性（分别为 90.06%、86.34% 和 84.38%）预测机械通气的需求。该预测是在实际插管事件前大约 3 天完成的。我们的模型还优于评估相同 X 射线图像的两位肺部和重症监护专家，并提供了 7.24%–13.25% 的增量准确度。结论 我们的深度学习模型准确预测了 COVID-19 患者住院期间早期对机械通气的需求。在针对 COVID-19 患者广泛采用有效的预防或治疗措施之前，我们的模型提供的预后分层可能非常有价值。作者可根据合理要求提供数据和代码。