Advertisement

Artificial Intelligence in the Imaging of Diffuse Lung Disease

Published:September 06, 2022DOI:https://doi.org/10.1016/j.rcl.2022.06.014

      Keywords

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribers receive full online access to your subscription and archive of back issues up to and including 2002.

      Content published before 2002 is available via pay-per-view purchase only.

      Subscribe:

      Subscribe to Radiologic Clinics
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • Hansell D.M.
        Classification of diffuse lung diseases: why and how.
        Radiology. 2013; 268: 628-640
        • Watadani T.
        • Sakai F.
        • Johkoh T.
        • et al.
        Interobserver variability in the CT assessment of honeycombing in the lungs.
        Radiology. 2013; 266: 936-944
        • Crossley D.
        • Renton M.
        • Khan M.
        • et al.
        CT densitometry in emphysema: a systematic review of its clinical utility.
        Int J Chron Obstruct Pulmon Dis. 2018; 13: 547-563
        • Kim G.B.
        • Jung K.H.
        • Lee Y.
        • et al.
        Comparison of shallow and deep learning methods on classifying the regional pattern of diffuse lung disease.
        J Digit Imaging. 2018; 31: 415-424
        • Mahon R.N.
        • Hugo G.D.
        • Weiss E.
        Repeatability of texture features derived from magnetic resonance and computed tomography imaging and use in predictive models for non-small cell lung cancer outcome.
        Phys Med Biol. 2018; 31: 415-424
        • Chen A.
        • Karwoski R.A.
        • Gierada D.S.
        • et al.
        Quantitative CT analysis of diffuse lung disease.
        Radiographics. 2020; 40: 28-43
        • Dictionary O.E.
        Artificial intelligence, n.
        in: Oxford English dictionary. Oxford University Press Online Dictionary, 2017 (Accessed 27 July 2022)
        • Suzuki K.
        Overview of deep learning in medical imaging.
        Radiological Phys Technol. 2017; 10: 257-273
      1. Oxford-English-Dictionary. deep learning n. In. Oxford English Dictionary 2022. Online Dictionary. Available at: www.oed.com. Accessed January 4,2022.

        • LeCun Y.
        • Bengio Y.
        • Hinton G.
        Deep learning.
        Nature. 2015; 521: 436
        • Hata A.
        • Schiebler M.L.
        • Lynch D.A.
        • et al.
        Interstitial lung abnormalities: state of the art.
        Radiology. 2021; 301: 19-34
        • Putman R.K.
        • Hatabu H.
        • Araki T.
        • et al.
        Association between interstitial lung abnormalities and all-cause mortality.
        JAMA. 2016; 315: 672-681
        • Bermejo-Peláez D.
        • Ash S.Y.
        • Washko G.R.
        • et al.
        Classification of interstitial lung abnormality patterns with an ensemble of deep convolutional neural networks.
        Scientific Rep. 2020; 10: 338
        • Bartholmai B.J.
        • Raghunath S.
        • Karwoski R.A.
        • et al.
        Quantitative computed tomography imaging of interstitial lung diseases.
        J Thorac Imaging. 2013; 28: 298-307
        • Jankharia B.G.
        • Angirish B.A.
        Computer-Aided quantitative analysis in interstitial lung diseases - A pictorial review using CALIPER.
        Lung India. 2021; 38: 161-167
        • Chung J.H.
        • Adegunsoye A.
        • Cannon B.
        • et al.
        Differentiation of Idiopathic pulmonary fibrosis from connective tissue disease-related interstitial lung disease using quantitative imaging.
        J Clin Med. 2021; 10: 2663
        • Chung J.H.
        • Adegunsoye A.
        • Oldham J.M.
        • et al.
        Vessel-related structures predict UIP pathology in those with a non-IPF pattern on CT.
        Eur Radiol. 2021; 31: 7295-7302
        • Jacob J.
        • Bartholmai B.J.
        • Rajagopalan S.
        • et al.
        Predicting outcomes in idiopathic pulmonary fibrosis using automated computed tomographic analysis.
        Am J Respir Crit Care Med. 2018; 198: 767-776
        • Jacob J.
        • Bartholmai B.J.
        • Rajagopalan S.
        • et al.
        Mortality prediction in idiopathic pulmonary fibrosis: evaluation of computer-based CT analysis with conventional severity measures.
        Eur Respir J. 2017; 49: 1601011
        • Filev P.D.
        • Little B.P.
        • Duong P.T.
        Second-opinion reads in interstitial lung disease imaging: added value of subspecialty interpretation.
        J Am Coll Radiol. 2020; 17: 786-790
        • De Sadeleer L.J.
        • Meert C.
        • Yserbyt J.
        • et al.
        Diagnostic ability of a dynamic multidisciplinary discussion in interstitial lung diseases: a retrospective observational study of 938 cases.
        Chest. 2018; 153: 1416-1423
        • Monnier-Cholley L.
        • MacMahon H.
        • Katsuragawa S.
        • et al.
        Computerized analysis of interstitial infiltrates on chest radiographs: a new scheme based on geometric pattern features and Fourier analysis.
        Acad Radiol. 1995; 2: 455-462
        • Xu Y.
        • van Beek E.J.
        • Hwanjo Y.
        • et al.
        Computer-aided classification of interstitial lung diseases via MDCT: 3D adaptive multiple feature method (3D AMFM).
        Acad Radiol. 2006; 13: 969-978
        • Kim G.B.
        • Jung K.-H.
        • Lee Y.
        • et al.
        Comparison of shallow and deep learning methods on classifying the regional pattern of diffuse lung disease.
        J Digit Imaging. 2018; 31: 415-424
        • Christe A.
        • Peters A.A.
        • Drakopoulos D.
        • et al.
        Computer-aided diagnosis of pulmonary fibrosis using deep learning and CT images.
        Invest Radiol. 2019; 54: 627-632
        • Shaish H.
        • Ahmed F.S.
        • Lederer D.
        • et al.
        Deep learning of computed tomography virtual wedge resection for prediction of histologic usual interstitial pneumonitis.
        Ann Am Thorac Soc. 2020; 18: 51-59
        • Park B.
        • Park H.
        • Lee S.M.
        • et al.
        Lung segmentation on HRCT and volumetric CT for diffuse interstitial lung disease using deep convolutional neural networks.
        J Digit Imaging. 2019; 32: 1019-1026
        • Yoo S.J.
        • Yoon S.H.
        • Lee J.H.
        • et al.
        Automated lung segmentation on chest computed tomography images with extensive lung parenchymal abnormalities using a deep neural network.
        Korean J Radiol. 2021; 22: 476-488
        • Goh N.S.
        • Desai S.R.
        • Veeraraghavan S.
        • et al.
        Interstitial lung disease in systemic sclerosis: a simple staging system.
        Am J Respir Crit Care Med. 2008; 177: 1248-1254
        • Moore O.A.
        • Goh N.
        • Corte T.
        • et al.
        Extent of disease on high-resolution computed tomography lung is a predictor of decline and mortality in systemic sclerosis-related interstitial lung disease.
        Rheumatology (Oxford). 2013; 52: 155-160
        • Chassagnon G.
        • Vakalopoulou M.
        • Régent A.
        • et al.
        Deep learning-based approach for automated assessment of interstitial lung disease in systemic sclerosis on CT images.
        Radiol Artif Intell. 2020; 2: e190006
        • Chassagnon G.
        • Vakalopoulou M.
        • Régent A.
        • et al.
        Elastic Registration-driven Deep Learning for Longitudinal Assessment of Systemic Sclerosis Interstitial Lung Disease at CT.
        Radiology. 2021; 298: 189-198
        • Occhipinti M.
        • Bosello S.
        • Sisti L.G.
        • et al.
        Quantitative and semi-quantitative computed tomography analysis of interstitial lung disease associated with systemic sclerosis: a longitudinal evaluation of pulmonary parenchyma and vessels.
        PLoS One. 2019; 14: e0213444
        • Si-Mohamed S.A.
        • Nasser M.
        • Colevray M.
        • et al.
        Automatic quantitative computed tomography measurement of longitudinal lung volume loss in interstitial lung diseases.
        Eur Radiol. 2018; 31: 415-424
      2. Irvin J, Rajpurkar P, Ko M, et al. CheXpert: a large chest radiograph dataset with uncertainty labels and expert comparison. Proceedings of the AAAI Conference on Artificial Intelligence. 2019;33(01):590-597. Honolulu, Hawaii USA — January 27–February 1, 2019.

        • Depeursinge A.
        • Vargas A.
        • Platon A.
        • et al.
        Building a reference multimedia database for interstitial lung diseases.
        Comput Med Imaging Graph. 2012; 36: 227-238
        • NIH
        Lung tissue research Consortium.
        National Institutes of Health, 2022 (Available at:) (Accessed 1/02/2022)
        • Barbiero P.
        • Squillero G.
        • Tonda A.
        Modeling generalization in machine learning: a methodological and computational study.
        arXiv. 2018; 31: 415-424
        • McBee M.P.
        • Awan O.A.
        • Colucci A.T.
        • et al.
        Deep learning in radiology.
        Acad Radiol. 2018; 31: 415-424
        • Chartrand G.
        • Cheng P.M.
        • Vorontsov E.
        • et al.
        Deep learning: a primer for radiologists.
        RadioGraphics. 2017; 37: 2113-2131
        • Erickson B.J.
        • Korfiatis P.
        • Akkus Z.
        • et al.
        Machine learning for medical imaging.
        Radiographics. 2017; 37: 505-515
        • Richardson M.L.
        • Adams S.J.
        • Agarwal A.
        • et al.
        Review of artificial intelligence training tools and courses for radiologists.
        Acad Radiol. 2021; 28: 1238-1252
        • Goodfellow I.
        • Bengio Y.
        • Courville A.
        Deep learning.
        The MIT Press, Cambridge (MA)2016