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Editorial
. 2023 Apr;307(1):e222675.
doi: 10.1148/radiol.222675. Epub 2022 Dec 13.

Artificial Intelligence Analysis of Bronchiectasis Is Predictive of Outcomes in Chronic Obstructive Pulmonary Disease

Mark L Schiebler  1 , Joon Beom Seo  1
Affiliations
Editorial

Artificial Intelligence Analysis of Bronchiectasis Is Predictive of Outcomes in Chronic Obstructive Pulmonary Disease

Mark L Schiebler et al. Radiology. 2023 Apr.
No abstract available

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Conflict of interest statement

Disclosures of conflicts of interest: M.L.S. Grants from the National Institutes of Health, National Heart, Lung, and Blood Institute, Severe Asthma Research Program III and IV; author has four patents; deputy editor for Radiology; shareholder in X-Vax, Stemina Biomarker Discovery, Elucida Oncology, Elucent Medical. J.B.S. Royalties from Coreline Soft; patents issued from South Korea; stock/stock options from Coreline Soft, Premedius.

Figures

Mark L. Schiebler, MD is a professor of cardiothoracic imaging in the Department of Radiology at the University of Wisconsin–Madison. His research interests are in the diseases of the small airways and coronary arteries and how imaging biomarkers and computational fluid dynamics can be used for more cost-effective patient outcomes. Dr Schiebler is a member of the Fleischner Society, STR, ISMRM, RSNA, and SMRA, and is the current chairman of the board of the International Workshop for Pulmonary Functional Imaging. He also serves as a deputy editor for Radiology.
Mark L. Schiebler, MD is a professor of cardiothoracic imaging in the Department of Radiology at the University of Wisconsin–Madison. His research interests are in the diseases of the small airways and coronary arteries and how imaging biomarkers and computational fluid dynamics can be used for more cost-effective patient outcomes. Dr Schiebler is a member of the Fleischner Society, STR, ISMRM, RSNA, and SMRA, and is the current chairman of the board of the International Workshop for Pulmonary Functional Imaging. He also serves as a deputy editor for Radiology.
Joon Beom Seo, MD is a professor of radiology at University of Ulsan College of Medicine in South Korea. He researches applications for various technologies such as image processing, robotics, three-dimensional printing, and artificial intelligence to improve radiologic practice. He researched quantification of chronic obstructive pulmonary disease and interstitial lung disease at CT, evaluation of combined perfusion and ventilation of the lung using dual-energy CT, lung cancer radiomics, and AI techniques for lung imaging. He founded the Korean Society of Artificial Intelligence in Medicine in 2018 and was its first president. He is the president of the International Workshop for Pulmonary Functional Imaging.
Joon Beom Seo, MD is a professor of radiology at University of Ulsan College of Medicine in South Korea. He researches applications for various technologies such as image processing, robotics, three-dimensional printing, and artificial intelligence to improve radiologic practice. He researched quantification of chronic obstructive pulmonary disease and interstitial lung disease at CT, evaluation of combined perfusion and ventilation of the lung using dual-energy CT, lung cancer radiomics, and AI techniques for lung imaging. He founded the Korean Society of Artificial Intelligence in Medicine in 2018 and was its first president. He is the president of the International Workshop for Pulmonary Functional Imaging.
See this image and copyright information in PMC

Comment on

  • Artificial Intelligence-based CT Assessment of Bronchiectasis: The COPDGene Study.
    Díaz AA, Nardelli P, Wang W, San José Estépar R, Yen A, Kligerman S, Maselli DJ, Dolliver WR, Tsao A, Orejas JL, Aliberti S, Aksamit TR, Young KA, Kinney GL, Washko GR, Silverman EK, San José Estépar R. Díaz AA, et al. Radiology. 2023 Apr;307(1):e221109. doi: 10.1148/radiol.221109. Epub 2022 Dec 13. Radiology. 2023. PMID: 36511808 Free PMC article.

References

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