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Prospective machine learning CT quantitative evaluation of idiopathic pulmonary fibrosis in patients undergoing anti-fibrotic treatment using low- and ultra-low-dose CT.
Koo, C W; Larson, N B; Parris-Skeete, C T; Karwoski, R A; Kalra, S; Bartholmai, B J; Carmona, E M.
Affiliation
  • Koo CW; Department of Radiology, Mayo Clinic, Rochester, MN, USA. Electronic address: koo.chiwan@mayo.edu.
  • Larson NB; Department of Quantitative Health Sciences, Division of Clinical Trials and Biostatistics, Mayo Clinic, Rochester, MN, USA.
  • Parris-Skeete CT; Department of Radiology, Mayo Clinic, Rochester, MN, USA.
  • Karwoski RA; Biomedical Imaging Resources, Research Applications Solutions, Mayo Clinic, Rochester, MN, USA.
  • Kalra S; Department of Medicine, Division of Pulmonary, Critical Care and Sleep Medicine, Mayo Clinic, Rochester, MN, USA.
  • Bartholmai BJ; Department of Radiology, Mayo Clinic, Rochester, MN, USA.
  • Carmona EM; Department of Medicine, Division of Pulmonary, Critical Care and Sleep Medicine, Mayo Clinic, Rochester, MN, USA.
Clin Radiol ; 77(3): e208-e214, 2022 03.
Article in En | MEDLINE | ID: mdl-34887070
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Tomography, X-Ray Computed / Idiopathic Pulmonary Fibrosis / Machine Learning Type of study: Risk_factors_studies Limits: Aged / Aged80 / Female / Humans / Male / Middle aged Language: En Journal: Clin Radiol Year: 2022 Document type: Article Country of publication: United kingdom
Fulltext
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Tomography, X-Ray Computed / Idiopathic Pulmonary Fibrosis / Machine Learning Type of study: Risk_factors_studies Limits: Aged / Aged80 / Female / Humans / Male / Middle aged Language: En Journal: Clin Radiol Year: 2022 Document type: Article Country of publication: United kingdom