<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Blake VanBerlo</style></author><author><style face="normal" font="default" size="100%">Derek Wu</style></author><author><style face="normal" font="default" size="100%">Brian Li</style></author><author><style face="normal" font="default" size="100%">Marwan A. Rahman</style></author><author><style face="normal" font="default" size="100%">Gregory Hogg</style></author><author><style face="normal" font="default" size="100%">Bennett VanBerlo</style></author><author><style face="normal" font="default" size="100%">Jared Tschirhart</style></author><author><style face="normal" font="default" size="100%">Alex Ford</style></author><author><style face="normal" font="default" size="100%">Jordan Ho</style></author><author><style face="normal" font="default" size="100%">Joseph McCauley</style></author><author><style face="normal" font="default" size="100%">Benjamin Wu</style></author><author><style face="normal" font="default" size="100%">Jason Deglint</style></author><author><style face="normal" font="default" size="100%">Jaswin Hargun</style></author><author><style face="normal" font="default" size="100%">Rushil Chaudhary</style></author><author><style face="normal" font="default" size="100%">Chintan Dave</style></author><author><style face="normal" font="default" size="100%">Robert Arntfield</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Accurate assessment of the lung sliding artefact on lung ultrasonography using a deep learning approach</style></title><secondary-title><style face="normal" font="default" size="100%">Computers in Biology and Medicine</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2022</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.1016/j.compbiomed.2022.105953</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">148</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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’s focus on relevant regions on M-mode images. Our solution accurately distinguishes between the presence and absence of lung sliding artefacts on LUS.</style></abstract><issue><style face="normal" font="default" size="100%">0010-4825</style></issue></record></records></xml>