COVID-19 Severity Prediction from Chest X-ray Images using an Anatomy-Aware Deep Learning Model

Abstract Introduction: The Covid-19 pandemic has been adversely affecting the patient management systems in hospitals around the world. Radiological imaging, especially the chest x-ray and lung Computed Tomography (CT)-scans, play 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. Methods: 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. Results: 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. Conclusions: The results demonstrate the effectiveness of the proposed AA model in Covid-19 severity prediction from chest X-ray images. Implications for practice: The algorithm can be used in low-resource setting hospitals for Covid-19 severity prediction, especially where there is a lack of skilled radiologists.