Pneumothorax is a potentially life-threatening condition that can be rapidly and accurately assessed via the lung sliding artefact generated using lung ultrasound (LUS). The presence of lung sliding rules out a diagnosis of pneumothorax, while its presence warrants further investigation. We present a method to distinguish present from absent lung sliding that includes conversion of B-mode LUS videos to M-mode images and subsequent prediction using a 2D convolutional neural network. The classifier achieved mean sensitivity for absent lung sliding of 0.935 (SD 0.0034) and 0.832 (SD 0.039) on ten-fold cross validation respectively. On a holdout set consisting of separate patients, the classifier achieved 0.935 sensitivity and 0.875 specificity. The results add to the growing evidence that deep computer vision methods are useful in establishing automated LUS interpretation.