@article {31256,
	title = {Accurate assessment of the lung sliding artefact on lung ultrasonography using a deep learning approach},
	journal = {Computers in Biology and Medicine},
	volume = {148},
	year = {2022},
	abstract = {Pneumothorax is a potentially life-threatening condition that can be rapidly and accurately assessed via the lung sliding artefact generated using lung ultrasound (LUS). Access to LUS is challenged by user dependence and shortage of training. Image classification using deep learning methods can automate interpretation in LUS and has not been thoroughly studied for lung sliding. Using a labelled LUS dataset from 2 academic hospitals, clinical B-mode (also known as brightness or two-dimensional mode) videos featuring both presence and absence of lung sliding were transformed into motion (M) mode images. These images were subsequently used to train a deep neural network binary classifier that was evaluated using a holdout set comprising 15\% of the total data. Grad-CAM explanations were examined. Our binary classifier using the EfficientNetB0 architecture was trained using 2535 LUS clips from 614 patients. When evaluated on a test set of data uninvolved in training (540 clips from 124 patients), the model performed with a sensitivity of 93.5\%, specificity of 87.3\% and an area under the receiver operating characteristic curve (AUC) of 0.973. Grad-CAM explanations confirmed the model{\textquoteright}s focus on relevant regions on M-mode images. Our solution accurately distinguishes between the presence and absence of lung sliding artefacts on LUS.},
	url = {https://doi.org/10.1016/j.compbiomed.2022.105953},
	author = {Blake VanBerlo and Derek Wu and Brian Li and Marwan A. Rahman and Gregory Hogg and Bennett VanBerlo and Jared Tschirhart and Alex Ford and Jordan Ho and Joseph McCauley and Benjamin Wu and Jason Deglint and Jaswin Hargun and Rushil Chaudhary and Chintan Dave and Robert Arntfield}
}
