A Challenge for Emphysema Quantification Using a Deep Learning Algorithm With Low-dose Chest Computed Tomography

The purpose of this study was to identify clinically relevant deep learning algorithms for emphysema quantification using low-dose chest computed tomography (LDCT) through an invitation-based competition. The Korean Society of Imaging Informatics in Medicine (KSIIM) organized this challenge, where seven research teams, including Coreline Soft, participated. A total of 558 CT scan pairs from nine hospitals were collected to develop algorithms that converted LDCT to simulate standard-dose CT (SDCT) values. The agreement between SDCT and LDCT was evaluated using various statistical measures. The results showed that emphysema quantification with LDCT was feasible through deep learning-based CT conversion strategies. Coreline Soft participated in the challenge and demonstrated strong performance, contributing to the development of effective AI solutions for emphysema quantification in real-world clinical settings.