Evaluating deep learning approaches for AI-assisted lung ultrasound diagnosis: an international multi-center and multi-scanner study

Lung ultrasound (LUS) interpretation is often subjective and operator-dependent, motivating the development of automated, artificial intelligence (AI)-based methods. This international, multi-center study evaluated two distinct deep learning approaches for automated LUS severity scoring for pulmonary infections caused by COVID-19: a pre-trained classification model (CM) and a segmentation model based method (SM); assessing performance at video, exam, and prognostic levels. Two datasets were analyzed: one comprising data from multiple scanners and another using data from a single scanner. Results showed that the SM achieved prognostic-level agreement with expert clinicians comparable to that of the CM. Furthermore, at the exam level, over 84% of examinations were classified with acceptable error (≤ 10 score difference) across both models and datasets, reaching both methods an agreement higher than 95% on the dataset acquired by a single scanner. The obtained results demonstrate the pote...