mHealth Research Group, Department of Biomedical Engineering, Bangladesh University of Engineering and Technology (BUET), Dhaka, 1205, Bangladesh.
Department of Radiology and Imaging, Bangabandhu Sheikh Mujib Medical University, Shahbagh, Dhaka, 1000, Bangladesh.
J Digit Imaging. 2023 Oct;36(5):2100-2112. doi: 10.1007/s10278-023-00861-6. Epub 2023 Jun 27.
The COVID-19 pandemic has been adversely affecting the patient management systems in hospitals around the world. Radiological imaging, especially chest x-ray and lung Computed Tomography (CT) scans, plays a vital role in the severity analysis of hospitalized COVID-19 patients. However, with an increasing number of patients and a lack of skilled radiologists, automated assessment of COVID-19 severity using medical image analysis has become increasingly important. Chest x-ray (CXR) imaging plays a significant role in assessing the severity of pneumonia, especially in low-resource hospitals, and is the most frequently used diagnostic imaging in the world. Previous methods that automatically predict the severity of COVID-19 pneumonia mainly focus on feature pooling from pre-trained CXR models without explicitly considering the underlying human anatomical attributes. This paper proposes an anatomy-aware (AA) deep learning model that learns the generic features from x-ray images considering the underlying anatomical information. Utilizing a pre-trained model and lung segmentation masks, the model generates a feature vector including disease-level features and lung involvement scores. We have used four different open-source datasets, along with an in-house annotated test set for training and evaluation of the proposed method. The proposed method improves the geographical extent score by 11% in terms of mean squared error (MSE) while preserving the benchmark result in lung opacity score. The results demonstrate the effectiveness of the proposed AA model in COVID-19 severity prediction from chest X-ray images. The algorithm can be used in low-resource setting hospitals for COVID-19 severity prediction, especially where there is a lack of skilled radiologists.
新冠疫情对全球医院的患者管理系统产生了不利影响。放射影像学，特别是胸部 X 光和肺部计算机断层扫描（CT）扫描，在住院 COVID-19 患者严重程度分析中发挥着至关重要的作用。然而，随着患者数量的增加和熟练放射科医生的缺乏，使用医学图像分析自动评估 COVID-19 严重程度变得越来越重要。胸部 X 光（CXR）成像在评估肺炎严重程度方面起着重要作用，尤其是在资源匮乏的医院，并且是世界上使用最广泛的诊断成像方式。以前的自动预测 COVID-19 肺炎严重程度的方法主要侧重于从预训练的 CXR 模型中进行特征汇集，而没有明确考虑潜在的人体解剖属性。本文提出了一种解剖感知（AA）深度学习模型，该模型考虑潜在的解剖信息，从 X 射线图像中学习通用特征。该模型利用预训练的模型和肺部分割掩模，生成一个包含疾病级别特征和肺部受累评分的特征向量。我们使用了四个不同的开源数据集，以及一个内部标注的测试集来训练和评估所提出的方法。所提出的方法在均方误差（MSE）方面将地理范围得分提高了 11%，同时保持了肺不透明度得分的基准结果。结果表明，所提出的 AA 模型在基于胸部 X 射线图像的 COVID-19 严重程度预测中是有效的。该算法可以在资源匮乏的医院环境中用于 COVID-19 严重程度预测，特别是在缺乏熟练放射科医生的情况下。
