“Quo Vadis Diagnosis”: Application of Informatics in Early Detection of Pneumothorax

Abstract

A pneumothorax is a condition that occurs in the lung region when air enters the pleural space—the area between the lung and chest wall—causing the lung to collapse and making it difficult to breathe. This can happen spontaneously or as a result of an injury. The symptoms of a pneumothorax may include chest pain, shortness of breath, and rapid breathing. Although chest X-rays are commonly used to detect a pneumothorax, locating the affected area visually in X-ray images can be time-consuming and prone to errors. Existing computer technology for detecting this disease from X-rays is limited by three major issues, including class disparity, which causes overfitting, difficulty in detecting dark portions of the images, and vanishing gradient. To address these issues, we propose an ensemble deep learning model called PneumoNet, which uses synthetic images from data augmentation to address the class disparity issue and a segmentation system to identify dark areas. Finally, the issue of the vanishing gradient, which becomes very small during back propagation, can be addressed by hyperparameter optimization techniques that prevent the model from slowly converging and poorly performing. Our model achieved an accuracy of 98.41% on the Society for Imaging Informatics in Medicine pneumothorax dataset, outperforming other deep learning models and reducing the computation complexities in detecting the disease.

Confiabilidad del ultrasonido para diagnóstico de neumotórax

“…El diagnóstico de neumotórax puede ser clínico, pero dada la baja sensibilidad del examen físico, se hace necesario la utilización de estudios de imagen (Kong et al 2015). La tomografía computarizada es el estándar para su diagnóstico, sin embargo dado a factores como al coste y al tiempo que lleva realizar este procedimiento, la radiografía de tórax se utiliza con más frecuencia, aun así, localizar el área afectada visualmente en las imágenes de rayos X puede ser lento y propenso a errores, además, algunos estudios han concluido que la RXT no es lo suficientemente sensible ya que puede no detectar hasta más de la mitad de los casos de neumotórax (Ball et al 2005;Husain et al 2012;Kumar et al 2023).…”