  Lung ultrasound imaging is reaching growing interest from the scientific
community. On one side, thanks to its harmlessness and high descriptive power,
this kind of diagnostic imaging has been largely adopted in sensitive
applications, like the diagnosis and follow-up of preterm newborns in neonatal
intensive care units. On the other side, state-of-the-art image analysis and
pattern recognition approaches have recently proven their ability to fully
exploit the rich information contained in these data, making them attractive
for the research community. In this work, we present a thorough analysis of
recent deep learning networks and training strategies carried out on a vast and
challenging multicenter dataset comprising 87 patients with different diseases
and gestational ages. These approaches are employed to assess the lung
respiratory status from ultrasound images and are evaluated against a reference
marker. The conducted analysis sheds some light on this problem by showing the
critical points that can mislead the training procedure and proposes some
adaptations to the specific data and task. The achieved results sensibly
outperform those obtained by a previous work, which is based on textural
features, and narrow the gap with the visual score predicted by the human
experts.
