## 1 Introduction
predictive value (94%) and specificity (95%), and a significant reduction in the delay for critical and urgent image findings. [PERSON] et al. [16] evaluated a commercially available deep-learning algorithm designed to classify four thoracic diseases (malignancy, active tuberculosis, pneumonia, and pneumothorax) and retrospectively compared its performance to that of the radiology residents, showing a lower specificity (69.6 vs. 98.1%) but higher sensitivity (88.7% vs. 65.6%), making it feasible for helping to reveal clinical abnormalities in an emergency scenario. [PERSON] et al. [17] trained a neural-network-based model to identify several disease patterns (cardiomegaly, pulmonary congestion, pleural effusion, pulmonary opacities, and atelectasis) using images from intensive care unit patients, with the aim of helping non-radiologist physicians to improve their interpretation in their clinical routine. [PERSON] et al. [18] developed an algorithm for the longitudinal follow-up of patients (with a focus on intensive care units), showing that the algorithm could effectively detect the presence or absence of changes given a pair of radiographs from the same patients, effectively detecting urgent findings and reducing radiologists' workload. [PERSON] et al. [19] trained an algorithm for the initial evaluation of patients with acute chest pain (ACP), in order to predict, using chest radiographs, the need of further cardiovascular or pulmonary tests, and showing that, at a highly sensitive threshold (99%), 14% of the patients could be deferred from further testing. Pneumothorax is a good example of this specific problem. It is an acute condition where air enters the pleural space (a virtual cavity between the lungs and the thoracic wall--ribs), happening spontaneously (with or without underlying lung disease) or after chest trauma [20] Figure 2. The air that fills the pleural space comprises the lung, causing partial or complete lung collapse and preventing lung expansion during breathing, ultimately preventing the correct gas exchange and leading to shortness of breath. If this situation is maintained over time, it can lead to severe complications, with the ultimate consequence of death. Figure 2: **(a)** Diagram of the pneumothorax presentation. Air is leaked to the space between the pleura and the lungs, collapsing the left lung. **(b)** Chest X-ray image from a posteoanterior view of a patient with left pneumothorax. **(c)** Dotted line showing the collapsed lung (pneumothorax line). **(d)** Treated pneumothorax from the same patient as **(b)** after placement of a chest tube. A plain chest radiograph is typically used for diagnosis confirmation, although a small pneumothorax may need a CT [21]. The treatment of acute pneumothorax generally needs immediate needle decompression (to manually extract the air from the pleural cavity) or insertion of a chest tube. Small pneumothoraces may be managed conservatively, as they can resolve spontaneously [20]. In this context, delays in the detection of pneumothorax are critical as they can lead to increased morphiortality. Automatic systems that detect pneumothorax can help in early detection and treatment, and, considering the severity of the condition, also help to avoid complications and potentially save lives. Machine-learning algorithms are considered Class II devices by the U.S. Food and Drug Administration (FDA) [22; 23] and Class IIa or IIb under the European Union Medical Device Regulation (EU MDR), depending on their intended use and associated risk [24]. That categorization means that, for clinical implementation, these algorithms must undergo robust evaluation to demonstrate their clinical benefit and safety. In recent years, the release of several datasets of chest X-rays (Indiana [25], NIH ChestX-ray14 [26], CheXpert [27], MIMIC-CXR [28], PadChest [29], VinDr-CXR [30], etc.