A lines and B lines are artifacts visible in lung ultrasound (LUS) examinations that are routinely used to identify normal and pathological lung tissue respectively. We present a method to distinguish between normal and abnormal LUS clips that includes a convolutional neural network for image classification and a custom clip prediction method that ingests the network’s outputs. The image classifier achieved a mean AUC of 0.964 (SD 0.019) upon cross validation and an AUC of 0.926 on a holdout set from an external centre. With particular hyperparameter values, the clip-based algorithm achieves a recall of 0.90 and true negative rate of 0.92 on the external dataset. The results warrant further investigation of diagnostic tools aided by computer vision in LUS interpretation.