Feature detection and pneumonia diagnosis based on clinical lung ultrasound imagery using deep learning

Pneumonia is a common disease with both high morbidity and mortality. The diagnosis of pneumonia remains a clinical challenge, especially in low resource settings where prevalence is high, diagnostic devices are limited, and doctors are scarce. Lung ultrasound has been identified as a useful and low-cost tool for pneumonia diagnosis in many studies. In the present work, we first developed a convolutional neural network (CNN)-based deep learning algorithm to automatically identify four key features linked to lung conditions: pleural line, B-line, consolidation, and pleural effusion. The algorithm was trained using ultrasound data collected from over 150 pediatric and adult patients, with features annotated by expert sonographers. A single shot detection (SSD) framework was developed to detect those features in each video frame image. We then explored the accuracy of diagnosing pneumonia based on one or more lung ultrasound features, using CT as a gold standard. Our results indicate that deep learning algor...