Spectral-based pneumonia detection tool using ultrasound data from pediatric populations

Pediatric Pneumonia is one of the principal causes of death by year on children under the age of five worldwide. The diagnosis is commonly made by clinical criteria with support from imaging tools like radiography. Lung ultrasound has been considered a low-cost and portable alternative for pneumonia imaging; however, interpretation is subjective and requires adequate training. In the present work, a pneumonia detection algorithm based on the measurement of the fundamental bandwidth downshift over depth of ultrasound radiofrequency (RF) signals is presented. RF-data was obtained from lung ultrasound samples of children aged between six months and five years. Sampling was performed using a 6.6 MHz linear transducer. The sample consisted of 10 positive-and 10 negative-diagnosed RF cine-loops selected by a medical expert and captured in a local pediatric health institute. For each frame, several regions of interest were outlined starting from the pleural line. Corresponding functions for each RF-line of the maximum frequency decrement rate over depth from the fundamental spectra at a fixed bandwidth were estimated and linearly fitted. Finally, a descriptor function was build concatenating all fitted values from the RF-lines for each frame respectively. Each descriptor function was later thresholded to differentiate between healthy and pneumonic regions frame-wise. The optimal threshold was found to be 0.46 MHz/cm and was selected based on a receiver operator characteristic (ROC) curve analysis. The results revealed an accuracy rate higher than 90% on the sample.