# Detecting the Absence of Lung Sliding in Lung Ultrasound Using Deep Learning
## 1 Introduction
### Pneumothorax and Detection of Lung Sliding Motion  
Pneumothorax is an abnormal collection of air in the pleural space [7]. Lung sliding is an apparent motion of visceral and parietal pleura in LUS. The absence of a lung sliding pattern in LUS indicates pneumothorax [8]. Pneumothorax detection and the detection of the absence of lung sliding motion are often used interchangeably in research. However, [PERSON] and [PERSON] [6] have clearly stated that while lung sliding absence was observed in 100% of pneumothorax cases, there may be different causes for lung sliding absence, such as hemithorax with lung sliding absence in 8.8% of cases. Furthermore, the BLUE protocol [9], which is widely accepted by physicians, does not conclusively diagnose pneumothorax solely from the absence of lung sliding: Further diagnostic methods (e.g., lung point detection) must be used to confirm the diagnosis. Therefore, the binary classification of seashore/barcode patterns in M-mode images and the classification of visceral and parietal pleura motion in brightness mode (B-mode) videos is not a pneumothorax detection but, rather, the detection of the absence of lung sliding motion. We perform pneumothorax detection if we have training data for two pathologies that can manifest with the absence of lung sliding, e.g., pneumothorax and hemithorax, or we detect a lung point with the absence of lung sliding.  
[PERSON] et al. [10] presented a dataset of porcine LUSs with 343 M-mode videos and 364 B-mode images. The dataset contains control samples and samples with varying volumes of air injected into pleural space, causing pneumothorax. They presented the iFAST algorithm for the detection of pneumothorax; they achieved sensitivity of 98% and specificity of 95% for M-mode images, and sensitivity of 86% and specificity of 85%for B-mode videos. [PERSON] et al. [11] used an extended version of this dataset with 420 B-mode videos and 404 M-mode images, using three CNNs for the classification of pneumothorax: VGG16, VGG19, and ResNet50. The best-performing model was VGG16. For the M-mode images, they increased sensitivity to 98% and specificity to 96%; for the B-mode videos, they achieved an improvement of 97% and 99% in comparison with the iFAST algorithm. However, there is no evidence of a patient-wise split in the dataset. Therefore, frames belonging to one sample/patient can be found in the training, validation, and testing datasets.  
[PERSON] et al. [12] detected lung abnormalities with CNNs in a porcine LUS dataset. While, for the detection of B-lines, merged B-lines, consolidation, and pleural effusion, they applied single-shot detectors based on R-CNN [13], pneumothorax was detected with an Inception V3 CNN using simulated M-mode images. The video-based pneumothorax detection achieves 93% sensitivity and specificity for the 35 videos. The input resolution of simulated M-mode images and how the aggregation from M-mode images to video-based prediction is made was not stated in the article.  
[PERSON] et al. [14] developed four methods for the detection of lung sliding absence: M-mode, SOFT, Fusion, and LSTM. Mean sensitivity of their classifiers ranges from 78% to 84%, and mean specificity ranges from 82% to 87%. Their LUS dataset consists of samples from four porcines and totals 252 videos. Patient-wise split is handled by fourfold cross-validation whereby, at each K-fold, one of the four animals was left out of the training set.