Chronic Obstructive Pulmonary Disease
Chronic Obstructive Pulmonary Disease (COPD) is a chronic inflammatory lung disease causing airflow obstruction, a leading cause of death worldwide. Current research focuses on improving early diagnosis and monitoring disease progression using machine learning, particularly deep learning models like convolutional neural networks and vision transformers, applied to various data modalities including spirograms, cough sounds, chest X-rays, and CT scans. These advancements aim to enhance diagnostic accuracy, personalize treatment plans, and potentially predict exacerbations, ultimately improving patient outcomes and reducing the significant healthcare burden associated with COPD.
Papers
Fractional dynamics foster deep learning of COPD stage prediction
Chenzhong Yin, Mihai Udrescu, Gaurav Gupta, Mingxi Cheng, Andrei Lihu, Lucretia Udrescu, Paul Bogdan, David M Mannino, Stefan Mihaicuta
Optimizing Convolutional Neural Networks for Chronic Obstructive Pulmonary Disease Detection in Clinical Computed Tomography Imaging
Tina Dorosti, Manuel Schultheiss, Felix Hofmann, Johannes Thalhammer, Luisa Kirchner, Theresa Urban, Franz Pfeiffer, Florian Schaff, Tobias Lasser, Daniela Pfeiffer