Towards Automatic Detection of Pneumothorax in Emergency Care with Deep Learning Using Multi-Source Chest X-ray Data

Santiago Ibañez Caturla (), Juan de Dios Berná Mestre and Oscar Martinez Mozos ()
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Santiago Ibañez Caturla: Departamento de Radiología, Hospital Clínico Universitario Virgen de la Arrixaca, 30120 Murcia, Spain
Juan de Dios Berná Mestre: Departamento de Radiología, Hospital Clínico Universitario Virgen de la Arrixaca, 30120 Murcia, Spain
Oscar Martinez Mozos: Escuela Técnica Superior de Ingeniería y Diseño Industrial, Universidad Politécnica de Madrid, 28012 Madrid, Spain

Future Internet, 2025, vol. 17, issue 7, 1-28

Abstract: Pneumothorax is a potentially life-threatening condition defined as the collapse of the lung due to air leakage into the chest cavity. Delays in the diagnosis of pneumothorax can lead to severe complications and even mortality. A significant challenge in pneumothorax diagnosis is the shortage of radiologists, resulting in the absence of written reports in plain X-rays and, consequently, impacting patient care. In this paper, we propose an automatic triage system for pneumothorax detection in X-ray images based on deep learning. We address this problem from the perspective of multi-source domain adaptation where different datasets available on the Internet are used for training and testing. In particular, we use datasets which contain chest X-ray images corresponding to different conditions (including pneumothorax). A convolutional neural network (CNN) with an EfficientNet architecture is trained and optimized to identify radiographic signs of pneumothorax using those public datasets. We present the results using cross-dataset validation, demonstrating the robustness and generalization capabilities of our multi-source solution across different datasets. The experimental results demonstrate the model’s potential to assist clinicians in prioritizing and correctly detecting urgent cases of pneumothorax using different integrated deployment strategies.

Keywords: pneumothorax; deep learning; chest X-ray; medical imaging; artificial intelligence; radiography; computer-aided diagnosis (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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