## 2 Materials and Methods
### Datasets  
We performed a review of all public chest X-ray datasets available, to include as much relevant information as possible, with focus on datasets containing pneumothorax labels. We discarded datasets containing only pediatric population (VinDr-PCXR [40, 41]), datasets that we were unable to download or that were currently unavailable (CXR-AL14 [42] and CANDID-II [43]) or other datasets with labels not fully relevant to our analysis (PLCO, NLST, VinDr-RibXR, datasets focusing only on tuberculosis detection, etc.). We finally included 11 datasets in our work:  
* ChestX-ray14 ('NIH') [26]: This was the first dataset with a massive amount of images, released in 2016 by the US National Institute of Health (NIH). It contains 112,120 frontal images from 30,805 individual patients, containing 14 different labels (amongst them, 5302 labelled with pneumothorax (4.73%) and 60,361 with no findings) labelled mainly via NLP techniques. A small subset of the dataset also containsbounding boxes for disease localization. Posterior research and expert labelling of the dataset changed the final pneumothorax count to 5138 (4.58%). * CheXpert [27; 44]: This was released in 2019 by Stanford University (US), containing 224,316 frontal and lateral images from 65,240 patients, using also 14 labels (most of them overlapping with the ones from NIH dataset). It has 19,466 pneumothorax (8.68%) and 22,528 normal images (10.04%). It also contains a label 'Support Devices' for external devices such as pacemakers, endotracheal tubes, valves, catheters, etc., and introduces the possibility of uncertainty of a label ('\(-\)1' value). Labels were extracted via NLP, and they have recently released an extension with radiology reports included [32]. * MIMIC-CXR [28; 45]: This was released in 2019 by the Massachusetts Institute of Technology (MIT, US); it contains 377,110 multi-view images from 65,379 patients, using the same labels and NLP labeler as the previous dataset, but also releasing the radiology reports and original images in DICOM format. It contains 14,239 pneumothorax images (3.78%) and 143,363 normal images (38.02%). * PadChest [29]: This was made public in 2019 by University of Alicante (Spain); it comprises 160,868 multi-view images from 67,625 unique patients, containing the Spanish radiology report and the extracted hierarchical labels using Unified Medical Language System (UMLS) terminology. A subset of the dataset is also manually labeled. It contains 851 pneumothorax images (0.52%, 411 of them manually labeled) and 50,616 normal images (31.47%). * VinDr-CXR [30; 46]: This was released by VinBigData in 2022; it contains 18,000 frontal images from two Vietnamese hospitals, manually annotated from radiologists, using both global and local labels. It contains 12,657 normal images (70.3%) and 76 pneumothorax images (0.004%). The dataset was released in DICOM format. * SIIM-ACR Pneumothorax Detection Challenge [47]: This dataset was released in 2019 by the Society of Imaging Informatics in Medicine (SIIM) and American College of Radiology (ACR) for a competition focused on detecting pneumothorax in chest radiographs using segmentation. It contains 12,047 frontal images--9378 (78%) without pneumothorax and 2669 with pneumothorax (22%)--and their corresponding segmentation masks. * BRAX [48; 49]: This was released in 2022, and contains 40,967 frontal and lateral images from 18,442 patients from the Hospital Israelita Albert Einstein (Brazil). They adapted the NLP labeler used in CheXpert and MIMIC-CXR to Portuguese, thus extracting the same 14 disease labels. Original DICOM files were also publicly released. It contains 214 pneumothorax images (0.52%) and 29,009 images without findings (71%). * Indiana [25; 50]: This was the first multilabel chest X-ray dataset, released in 2012 by Indiana University (US), and contains 7470 images (frontal and lateral) from 3851 studies coming from two hospitals from the Indiana region, along with the radiology reports and MeSH codification.