Deep learning and the evaluation of pulmonary fibrosis
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The Walsh algorithm study highlighted limitations including limited number of scans in the training set and potential bias from the training set being labelled by one specialist only.
“However, this study also highlighted some limitations of this specific algorithm, such as the limited number of scans in the training set, and the potential bias introduced by the fact that the training set was labelled by one specialist only (Levin, 2018).”
From Pulmonary fibrosis: Emerging diagnostic and therapeutic strategies (2023) · cites this paper as Levin, 2018
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Raghu, G., Collard, H. R., Egan, J. J., Martinez, F. J., Behr, J., Brown, K. K., Colby, T. V., Cordier, J.-F., Flaherty, K. R., Lasky, J. A., et al. (2011) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.2009-040gl · Cited by 7,186 · see citing works
Deo, R. C. (2015) · Circulation · doi:10.1161/circulationaha.115.001593 · Cited by 3,466 · see citing works
Machine learning for medical imaging
Erickson, B. J., Korfiatis, P., Akkus, Z., & Kline, T. L. (2017) · RadioGraphics · doi:10.1148/rg.2017160130 · Cited by 1,672 · see citing works
Deep Learning in Medical Imaging: General Overview
Lee, J., Jun, S., Cho, Y.-W., Lee, H., Kim, G. B., Seo, J. B., & Kim, N. (2017) · Korean Journal of Radiology · doi:10.3348/kjr.2017.18.4.570 · Cited by 1,287 · see citing works
Diagnostic criteria for idiopathic pulmonary fibrosis: a Fleischner Society White Paper
Lynch, D. A., Sverzellati, N., Travis, W. D., Brown, K. K., Colby, T. V., Galvin, J. R., Goldin, J. G., Hansell, D. M., Inoue, Y., Johkoh, T., et al. (2018) · The Lancet Respiratory Medicine · doi:10.1016/s2213-2600(17)30433-2 · Cited by 1,030 · see citing works
McBee, M. P., Awan, O. A. R., Colucci, A. T., Ghobadi, C. W., Kadom, N., Kansagra, A. P., Tridandapani, S., & Auffermann, W. F. (2018) · Academic Radiology · doi:10.1016/j.acra.2018.02.018 · Cited by 502 · see citing works
Interobserver Variability in the CT Assessment of Honeycombing in the Lungs
Watadani, T., Sakai, F., Johkoh, T., Noma, S., Akira, M., Fujimoto, K., Bankier, A. A., Lee, K. S., Müller, N. L., Song, J., et al. (2012) · Radiology · doi:10.1148/radiol.12112516 · Cited by 412 · see citing works
Walsh, S. L. F., Calandriello, L., Silva, M., & Sverzellati, N. (2018) · The Lancet Respiratory Medicine · doi:10.1016/s2213-2600(18)30286-8 · Cited by 390 · see citing works
Interobserver agreement for the ATS/ERS/JRS/ALAT criteria for a UIP pattern on CT
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CT in Idiopathic Pulmonary Fibrosis: Diagnosis and Beyond
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Pulmonary fibrosis: Emerging diagnostic and therapeutic strategies
Selvarajah, B., Platé, M., & Chambers, R. C. (2023) · Molecular Aspects of Medicine · doi:10.1016/j.mam.2023.101227 · Cited by 57 · see citing works
- Authors
- Levin, D. L.
- Affiliations
- Mayo Clinic (US)
- Corresponding author
- Levin, David L
- ORCID
- Levin, David L
- Venue
- The Lancet Respiratory Medicine
- Publisher
- Elsevier BV
- Published
- 2018-11
- Volume
- 6
- Issue
- 11
- Pages
- 803-805
- ISSN
- 2213-2600 (ISSN-L)
- PMC
- PMC6293056
- OpenAlex
- W2891082731
- Cited by
- 16
- References
- 11
- Citations by year
- 2025: 4 · 2023: 5 · 2022: 1 · 2021: 5 · 2019: 1
- FWCI
- 1.13
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- The Lancet Respiratory Medicine (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Interstitial Lung Diseases and Idiopathic Pulmonary Fibrosis
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Medical Imaging and Pathology Studies
- Keywords
- pulmonary fibrosis, interstitial lung disease, deep learning, computed tomography, honeycombing, diagnostic criteria
- MeSH terms
- Deep Learning, Humans, Pulmonary Fibrosis, Tomography, X-Ray Computed, Cohort Studies
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