Ricerc@Sapienza

Background: community-acquired pneumonia (CAP) represents one of the most common infectious diseases among children. Diagnosis of CAP is mainly clinical. Chest X-ray (CXR) remains the gold standard for the diagnosis in severe or controversial conditions. Recently, some authors focused on the application of ultrasound in lung diseases, but the role of Lung Ultrasound in the diagnosis of CAP is still debated. We aimed to study the concordance between LUS and CXR in evaluating specific signs of CAP.

In recent years, computer-assisted diagnostic systems have gained increasing interest through the use of deep learning techniques. In this work we show how it is possible to classify X-ray images through a multi-input convolutional neural network. The use of clinical information together with the images allowed to obtain better results than those present in the literature on the same data.

In parallel with the vast medical research on clinical treatment of COVID-19, an important action to have the disease completely under control is to carefully monitor the patients. What the detection of COVID-19 relies on most is the viral tests, however, the study of X-rays is helpful due to the ease of availability. There are various studies that employ Deep Learning (DL) paradigms, aiming at reinforcing the radiography-based recognition of lung infection by COVID-19.