Deep Learning Estimation of Small Airways Disease from Inspiratory Chest CT: Clinical Validation, Repeatability, and Associations with Adverse Clinical Outcomes in COPD.

• Tác giả: Hira A Awan, Igor Barjaktarevic, R Graham Barr, Surya P Bhatt, Sandeep Bodduluri, Muhammad F A Chaudhary, Alejandro P Comellas, Christopher B Cooper, Jeffrey L Curtis, Craig J Galban, Sarah E Gerard, MeiLan Han, Nadia N Hansel, Eric A Hoffman, Jerry A Krishnan, Fernando J Martinez, Martha G Menchaca, Jill Ohar, Robert Paine, Joseph M Reinhardt, Luis G Vargas Buonfiglio
• Ngôn ngữ: eng
• Ký hiệu phân loại: 971 *Canada
• Thông tin xuất bản: United States : American journal of respiratory and critical care medicine , 2025
• Bộ sưu tập: NCBI
• ID: 699245

Tóm tắt

RATIONALE: Quantifying functional small airways disease (fSAD) requires additional expiratory computed tomography (CT) scan, limiting clinical applicability. Artificial intelligence (AI) could enable fSAD quantification from chest CT scan at total lung capacity (TLC) alone (fSAD OBJECTIVES: To evaluate an AI model for estimating fSAD METHODS: We analyzed 2513 participants from the SubPopulations and InteRmediate Outcome Measures in COPD Study (SPIROMICS). Using a randomly sampled subset ( MEASUREMENTS AND MAIN RESULTS: Inspiratory fSAD CONCLUSIONS: Small airways disease can be reliably assessed from a single inspiratory CT scan using generative AI, eliminating the need for an additional expiratory CT scan. fSAD estimation from inspiratory CT correlates strongly with fSAD