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Impact of a deep learning-based computer-aided detection system (BMAX) on CT findings and clinical parameters in screening-detected interstitial lung abnormalities: a cross-sectional study

   
@article{JTD125630,
	author = {Tsuguhiro Furukawa and Kimitaka Akaike and Hidenori Ichiyasu and Yuiko Masuda and Hiroko Okabayashi and Shohei Hamada and Aiko Masunaga and Kodai Kawamura and Kazuhiro Iyonaga and Takeshi Johkoh and Kiminori Fujimoto and Kazuya Ichikado and Takuro Sakagami},
	title = {Impact of a deep learning-based computer-aided detection system (BMAX) on CT findings and clinical parameters in screening-detected interstitial lung abnormalities: a cross-sectional study},
	journal = {Journal of Thoracic Disease},
	volume = {0},
	number = {0},
	year = {2026},
	keywords = {},
	abstract = {Background: BMAX is a deep learning-based computer-aided detection (CAD) system designed to identify chronic fibrosing interstitial lung disease (ILD) on chest radiographs. While its utility in clinical settings is recognized, the association between BMAX scores and specific computed tomography (CT) findings or clinical parameters in patients with interstitial lung abnormalities (ILAs) remains unclear. This study investigated the correlation between BMAX scores and specific CT findings and clinical parameters in patients with ILAs.Methods: In this observational, cross-sectional study, we enrolled 362 patients with ILAs detected during health screening. Patients were classified into high (≥0.3, n=286) or low (},
	issn = {2077-6624},	url = {https://jtd.amegroups.org/article/view/125630}
}