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

Hyewon Choi, MD, Hyungjin Kim, MD, PhD, Kwang Nam Jin, MD, PhD, Yeon Joo Jeong, MD, PhD, Kum Ju Chae, MD, PhD, Kyung Hee Lee, MD, PhD, Hwan Seok Yong, MD, PhD, Bomi Gil, MD, PhD, Hye-Jeong Lee, MD, PhD, Ki Yeol Lee, MD, PhD, Kyung Nyeo Jeon, MD, PhD, Jaeyoun Yi, PhD, Sola Seo, MSc , Chulkyun Ahn, Bsc, Joonhyung Lee, MSc, Kyuhyup Oh, PhD, and Jin Mo Goo, MD, PhD
Journal of Thoracic Imaging, 2022
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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.


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