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Quantitative CT and Artificial Intelligence in Chronic Lung Disease.

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Titel: Quantitative CT and Artificial Intelligence in Chronic Lung Disease.
Autoren: Oh AS; Department of Radiology, UCLA, Los Angeles, CA., Humphries SM; Department of Radiology, National Jewish Health, Denver, CO., Chung A; Department of Medicine, Division of Pulmonary, Critical Care, Allergy, and Immunology, UCLA, Los Angeles, CA., Weigt SS; Department of Medicine, Division of Pulmonary, Critical Care, Allergy, and Immunology, UCLA, Los Angeles, CA., Brown M; Department of Radiology, UCLA, Los Angeles, CA., Kim GHJ; Department of Radiology, UCLA, Los Angeles, CA., Lee D; Department of Radiology, UCLA, Los Angeles, CA., Belperio JA; Department of Medicine, Division of Pulmonary, Critical Care, Allergy, and Immunology, UCLA, Los Angeles, CA., Goldin JG; Department of Radiology, UCLA, Los Angeles, CA.
Quelle: Journal of thoracic imaging [J Thorac Imaging] 2025 Dec 22. Date of Electronic Publication: 2025 Dec 22.
Publication Model: Ahead of Print
Publikationsart: Journal Article
Sprache: English
Info zur Zeitschrift: Publisher: Lippincott Williams & Wilkins Country of Publication: United States NLM ID: 8606160 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1536-0237 (Electronic) Linking ISSN: 08835993 NLM ISO Abbreviation: J Thorac Imaging Subsets: MEDLINE
Imprint Name(s): Publication: 1998- : Philadelphia, PA : Lippincott Williams & Wilkins
Original Publication: [Gaithersburg, Md. : Aspen Systems Corp., c1985-
Abstract: Computed tomography (CT) is routinely used in diagnosing and managing patients with chronic lung diseases such as chronic obstructive pulmonary disease (COPD) and fibrosing interstitial lung disease (ILD). Visual assessment of disease morphology/phenotype and extent correlates with lung function and patient prognosis, but it is limited by reader subjectivity and interobserver variability. Quantitative CT (QCT) techniques based on density and texture-based features of the lungs have shown stronger correlations with physiologic and survival outcomes in both COPD and ILD cohort studies. Moreover, recent advances in computer processing capabilities have led to the implementation of machine and deep learning-based approaches, allowing for greater robustness and reproducibility beyond visual assessment and density-based methods. This review focuses on QCT and artificial intelligence (AI) techniques for COPD, ILD, and bronchiolitis obliterans syndrome in lung and hematopoietic stem cell transplant recipients. Current challenges and limitations for adoption of these techniques and future directions of QCT and AI in thoracic imaging are also discussed.
(Copyright © 2025 Wolters Kluwer Health, Inc. All rights reserved.)
Competing Interests: S.M.H. reports grants or contracts from NHLBI, Boehringer Ingelheim Pharmaceuticals and Perceptive, and US Patents 10,706,533; 11,4685,64; 11,494,902 and 11,922,626 (unlicensed and assigned to my institution). A.C. reports salary support from Boehringer Ingelheim Pharmaceuticals. M.B. is a board member of Voiant Clinical, LLC. GHK reports grant support from Boehringer Ingelheim Pharmaceuticals. The remaining authors declare no conflicts of interest.
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Contributed Indexing: Keywords: artificial intelligence; chronic obstructive pulmonary disease; interstitial lung disease; quantitative CT
Entry Date(s): Date Created: 20251219 Latest Revision: 20251219
Update Code: 20251220
DOI: 10.1097/RTI.0000000000000867
PMID: 41417666
Datenbank: MEDLINE
Beschreibung
Abstract:Computed tomography (CT) is routinely used in diagnosing and managing patients with chronic lung diseases such as chronic obstructive pulmonary disease (COPD) and fibrosing interstitial lung disease (ILD). Visual assessment of disease morphology/phenotype and extent correlates with lung function and patient prognosis, but it is limited by reader subjectivity and interobserver variability. Quantitative CT (QCT) techniques based on density and texture-based features of the lungs have shown stronger correlations with physiologic and survival outcomes in both COPD and ILD cohort studies. Moreover, recent advances in computer processing capabilities have led to the implementation of machine and deep learning-based approaches, allowing for greater robustness and reproducibility beyond visual assessment and density-based methods. This review focuses on QCT and artificial intelligence (AI) techniques for COPD, ILD, and bronchiolitis obliterans syndrome in lung and hematopoietic stem cell transplant recipients. Current challenges and limitations for adoption of these techniques and future directions of QCT and AI in thoracic imaging are also discussed.<br /> (Copyright © 2025 Wolters Kluwer Health, Inc. All rights reserved.)
ISSN:1536-0237
DOI:10.1097/RTI.0000000000000867