Artificial Intelligence-Based Emphysema Quantification in Routine Chest Computed Tomography: Correlation With Spirometry and Visual Emphysema Grading.

Journal: Journal of computer assisted tomography

PMID: 38110294

Abstract

OBJECTIVE: The aim of the study is to assess the correlation between artificial intelligence (AI)-based low attenuation volume percentage (LAV%) with forced expiratory volume in the first second to forced vital capacity (FEV1/FVC) and visual emphysema grades in routine chest computed tomography (CT). Furthermore, optimal LAV% cutoff values for predicting a FEV1/FVC < 70% or moderate to more extensive visual emphysema grades were calculated.

Authors

Damian Wiedbrauck
Maciej Karczewski

Department of Applied Mathematics, Wrocław University of Environmental and Life Sciences, Wroclaw, Poland.
Stefan O Schoenberg

University Medical Center Mannheim, Faculty of Medicine Mannheim, Institute of Clinical Radiology and Nuclear Medicine, Heidelberg University, Mannheim, Germany.
Christian Fink
Hany Kayed

Keywords

Adult Aged Artificial Intelligence Female Forced Expiratory Volume Humans Lung Male Middle Aged Pulmonary Emphysema Radiography, Thoracic Retrospective Studies Severity of Illness Index Spirometry Tomography, X-Ray Computed

External Resources

View on PubMed Access via DOI PubMed (38110294)

Artificial Intelligence-Based Emphysema Quantification in Routine Chest Computed Tomography: Correlation With Spirometry and Visual Emphysema Grading.

Abstract

Authors

Keywords

External Resources

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