الفهرس 
ANATOMY OF THE THORACIC WALL AND BREASTOnly 14 pages are availabe for public view
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Abstract
Background: chronic obstructive pulmonary disease is caused by two components: small-airway disease (obstructive bronchiolitis) and parenchymal destruction (emphysema). Although pulmonary function tests measure limitation of airflow, they are not able to differentiate between airway obstruction and emphysematous destruction. CT on the other hand can be used to both identify patients with emphysema as well as to monitor progression in patients with COPD. AI-based algorithms may well be suited for tasks such as pattern recognition on chest CT images and emphysema quantification. Aim of the work: To evaluate an artificial intelligence-based prototype algorithm for quantification of emphysema on chest CT compared with pulmonary function testing. Patients and Methods: This cross-sectional study was carried at radiodiagnosis department Ain Shams university hospitals. A total of 35 patients (22 males; 13 females) who underwent both chest CT acquisition and spirometry within 6 months were retrospectively included. The spirometry based Tiffeneau index (TI; calculated as the ratio of forced expiratory volume in the first second to forced vital capacity) was used to measure emphysema severity; a value less than 0.7 was considered to indicate airway obstruction. Lung volume analysis was automatically calculated using local artificial intelligence-based 3D reconstruction software and emphysema was quantified using attenuation-based threshold of (-950 HU). Percentage of Low attenuation area (LAA %) was reflected by artificial intelligence-based calculation of Goddard score. Emphysema quantification and TI were compared using the Spearman correlation coefficient.
Results: The mean TI for all patients was 0.77 ± 0. 22. The mean percentages of emphysema (LAA%) 20.54% ± 21.8%. AI-based emphysema quantification showed very strong correlation with TI (p < 0.0001), indicating that AI-based emphysema quantification meaningfully reflects clinical pulmonary physiology. Conclusion: AI-based, automated emphysema quantification shows good correlation with TI, potentially contributing to an image-based diagnosis, COPD categorization, follow-up, and treatment strategies planning.