Deep learning could improve lung ultrasound interpretation

Deep learning could improve real-time lung ultrasound interpretation, according to a study published January 29 in Ultrasonics. Researchers led by Lewis Howell, PhD, from the University of Leeds in the U.K. found that a deep learning model trained on lung ultrasound allowed for segmentation and characterization of artifacts on images when tested on a phantom model. “Machine learning and deep learning present an exciting opportunity to assist in the interpretation of lung ultrasound and other pathologies imaged using ultrasound,” Howell and colleagues wrote. Lung ultrasound in recent years has been highlighted in research as a safe, cost-effective imaging modality for evaluating lung health, and the COVID-19 pandemic saw lung ultrasound utilized more as a noninvasive imaging method. Still, the researchers noted that this method has its share of challenges, especially user variability. Howell and co-authors described a deep learning method for multi-class segmentation of objects such as ribs and pleural lines, as well as artifacts including A-lines, B-lines, and B-line confluences in ultrasound images of a lung training phantom. The team developed a version of the U-Net architecture for image segmentation to provide a balance between model speed and accuracy. It also used an ultrasound-specific augmentation pipeline during the training phase to improve the model’s ability to generalize unseen data, including geometric transformations and ultrasound-specific augment...
Source: AuntMinnie.com Headlines - Category: Radiology Authors: Tags: Subspecialties Artificial Intelligence Chest Radiology Source Type: news