COPD: artificial intelligence detects and quantifies anomalies on chest CT enabling prediction of disease severity
Author:
Publisher
Springer Science and Business Media LLC
Subject
Radiology, Nuclear Medicine and imaging,General Medicine
Link
https://link.springer.com/content/pdf/10.1007/s00330-024-10601-1.pdf
Reference10 articles.
1. Christenson SA, Smith BM, Bafadhel M, Putcha N (2022) Chronic obstructive pulmonary disease. Lancet 399:2227–2242. https://doi.org/10.1016/S0140-6736(22)00470-6
2. Bhatt SP, Washko GR, Hoffman EA et al (2019) Imaging advances in chronic obstructive pulmonary disease. Insights from the Genetic Epidemiology of Chronic Obstructive Pulmonary Disease (COPDGene) Study. Am J Respir Crit Care Med 199:286–301. https://doi.org/10.1164/rccm.201807-1351SO
3. Lynch DA, Moore CM, Wilson C et al (2018) CT-based visual classification of emphysema: association with mortality in the COPDGene Study. Radiology 288:859–866. https://doi.org/10.1148/radiol.2018172294
4. Humphries SM, Notary AM, Centeno JP et al (2020) Deep learning enables automatic classification of emphysema pattern at CT. Radiology 294:434–444. https://doi.org/10.1148/radiol.2019191022
5. González G, Ash SY, Vegas-Sánchez-Ferrero G et al (2018) Disease staging and prognosis in smokers using deep learning in chest computed tomography. Am J Respir Crit Care Med 197:193–203. https://doi.org/10.1164/rccm.201705-0860OC
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