Yusuke Shiraishi, Naoya Tanabe, Ryo Sakamoto, Tomoki Maetani, Shizuo Kaji, Hiroshi Shima, Satoru Terada, Kunihiko Terada, Kohei Ikezoe, Kiminobu Tanizawa, Tsuyoshi Oguma, Tomohiro Handa, Susumu Sato, Shigeo Muro, Toyohiro Hirai
BACKGROUND: Interstitial lung abnormalities (ILAs) on CT may affect the clinical outcomes in patients with chronic obstructive pulmonary disease (COPD), but their quantification remains unestablished. This study examined whether artificial intelligence (AI)-based segmentation could be applied to identify ILAs using two COPD cohorts. METHODS: ILAs were diagnosed visually based on the Fleischner Society definition. Using an AI-based method, ground-glass opacities, reticulations, and honeycombing were segmented, and their volumes were summed to obtain the percentage ratio of interstitial lung disease-associated volume to total lung volume (ILDvol%)...
April 23, 2024: BMC Pulmonary Medicine