  Over the last few decades, Lung Ultrasound (LUS) has been increasingly used
to diagnose and monitor different lung diseases in neonates. It is a non
invasive tool that allows a fast bedside examination while minimally handling
the neonate. Acquiring a LUS scan is easy, but understanding the artifacts
concerned with each respiratory disease is challenging. Mixed artifact patterns
found in different respiratory diseases may limit LUS readability by the
operator. While machine learning (ML), especially deep learning can assist in
automated analysis, simply feeding the ultrasound images to an ML model for
diagnosis is not enough to earn the trust of medical professionals. The
algorithm should output LUS features that are familiar to the operator instead.
Therefore, in this paper we present a unique approach for extracting seven
meaningful LUS features that can be easily associated with a specific
pathological lung condition: Normal pleura, irregular pleura, thick pleura,
Alines, Coalescent B-lines, Separate B-lines and Consolidations. These
artifacts can lead to early prediction of infants developing later respiratory
distress symptoms. A single multi-class region proposal-based object detection
model faster-RCNN (fRCNN) was trained on lower posterior lung ultrasound videos
to detect these LUS features which are further linked to four common neonatal
diseases. Our results show that fRCNN surpasses single stage models such as
RetinaNet and can successfully detect the aforementioned LUS features with a
mean average precision of 86.4%. Instead of a fully automatic diagnosis from
images without any interpretability, detection of such LUS features leave the
ultimate control of diagnosis to the clinician, which can result in a more
trustworthy intelligent system.
