Integrated assessment of total airway count and pneumonia volume on chest computed tomography as a prognostic biomarker for coronavirus disease
European RadiologyResearch Authors: Kensuke Nakagawara, Naoya Tanabe, Shotaro Chubachi, Tomoki Maetani, Yusuke Shiraishi, Takanori Asakura, Ho Namkoong, Hiromu Tanaka, Takashi Shimada, Shuhei Azekawa, Shiro Otake, Takahiro Fukushima, Mayuko Watase, Hideki Terai, Mamoru Sasaki, Soichiro Ueda, Yukari Kato, Norihiro Harada, Shoji Suzuki, Shuichi Yoshida, Hiroki Tateno, Yoshitake Yamada, Masahiro Jinzaki, Toyohiro Hirai, Yukinori Okada, Ryuji Koike, Makoto Ishii, Akinori Kimura, Seiya Imoto, Satoru Miyano, Seishi Ogawa, Takanori Kanai & Koichi FukunagaAIIM Authors: Kavya Vijayakumar, Ahmad IslambouliApproved by President Reda RiffiPublication Date: 12/24/2025Comprehensive Summary
Chest CT's are widely used to diagnose and assess disease severity in patients with COVID-19. In this study, chest CT images from 781 hospitalized patients were analyzed using an AI-based quantitative CT (AIQCT) that automatically segmented the lungs to quantify total airway count (TAC) and pneumonia volume. The study found that patients with critical COVID-19 outcomes exhibited significantly higher TAC than non-critical cases. Patients were further divided into four groups based on cutoff values for pneumonia volume and TAC, and their clinical characteristics and complications were compared. The group with both high pneumonia volume and high TAC demonstrated the highest frequency of critical outcomes. Overall, these results show that AI-based quantification of TAC and pneumonia volume on chest CT is an effective predictor of critical COVID-19 outcomes.
Outcomes and Implications
AI can effectively quantify TAC and pneumonia volume, and therefore predict the outcomes of COVID-19 patients. These results can be applied to other respiratory diseases as well.
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