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Deep Learning-based analysis of HRCT images for outcome prediction in pulmonary fibrosis: the added value over clinico-functional assessment in a prospective longitudinal study
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【作者】：Villone, F.; Calandriello, L.; Cicchetti, G. (...) 【来源】：Insights into Imaging, 2026, 17(1), 283 【摘要】：Purpose: Prognostic assessment in interstitial lung disease (ILD) is challenging, due to unpredictable behaviour and scarcity of validated progression markers. Clinico-functional progression criteria are limited by subjectivity and variability, while radiological progression relies on visual assessment, affected by inter-observer variability. Artificial intelligence(AI) on high-resolution Computed Tomography(HRCT) has emerged as a reliable tool for ILD assessment. This study evaluated deep-learning HRCT analysis with commercially available software in assessing progression and mortality in a real-life prospective ILD patients cohort. Methods or Background: HRCT scans from ILD patients (idiopathic pulmonary fibrosis [IPF] and non-IPF ILDs) at baseline and after 12±4 months were analyzed using AVIEW software (Coreline Soft). |
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(2025-6-4)
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