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
Year: 2026
Type: article
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 (<0.3, n=76) BMAX groups. CT findings and clinical parameters were compared between groups. Correlations between BMAX scores and relevant biomarkers, respiratory function, and imaging features were evaluated. Results: 104.4%, P<0.001), and elevated levels of disease severity markers [Krebs von den Lungen-6 (KL-6) and surfactant protein D (SP-D), both P<0.001] compared to the low-BMAX group. Correlation analysis confirmed that BMAX scores were significantly associated with these biomarkers, pulmonary-function parameters, and fibrosing CT patterns. Conclusions: BMAX scores are associated with fibrotic characteristics and indicators of ILA severity, suggesting their potential utility for identifying high-risk fibrotic imaging features in patients with ILA.
Source: Journal of Thoracic Disease
Language: English
Subfield: Pulmonary and Respiratory Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W7221150153