دورية أكاديمية
Comparison of Artificial Intelligence-Based Fully Automatic Chest CT Emphysema Quantification to Pulmonary Function Testing.
| العنوان: | Comparison of Artificial Intelligence-Based Fully Automatic Chest CT Emphysema Quantification to Pulmonary Function Testing. |
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| المؤلفون: | Fischer AM; Department of Radiology and Radiological Science, Division of Cardiovascular Imaging, Medical University of South Carolina, 25 Courtenay Dr MSC 226, Charleston, SC 29425.; Institute of Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany., Varga-Szemes A; Department of Radiology and Radiological Science, Division of Cardiovascular Imaging, Medical University of South Carolina, 25 Courtenay Dr MSC 226, Charleston, SC 29425., van Assen M; Department of Radiology and Radiological Science, Division of Cardiovascular Imaging, Medical University of South Carolina, 25 Courtenay Dr MSC 226, Charleston, SC 29425.; Center for Medical Imaging, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands., Griffith LP; Department of Radiology and Radiological Science, Division of Cardiovascular Imaging, Medical University of South Carolina, 25 Courtenay Dr MSC 226, Charleston, SC 29425., Sahbaee P; Siemens Medical Solutions, Malvern, PA., Sperl JI; Siemens Healthineers, Forchheim, Germany., Nance JW; Department of Radiology, Houston Methodist Hospital, Houston, TX., Schoepf UJ; Department of Radiology and Radiological Science, Division of Cardiovascular Imaging, Medical University of South Carolina, 25 Courtenay Dr MSC 226, Charleston, SC 29425. |
| المصدر: | AJR. American journal of roentgenology [AJR Am J Roentgenol] 2020 May; Vol. 214 (5), pp. 1065-1071. Date of Electronic Publication: 2020 Mar 04. |
| نوع المنشور: | Comparative Study; Journal Article; Research Support, Non-U.S. Gov't |
| اللغة: | English |
| بيانات الدورية: | Publisher: American Roentgen Ray Society Country of Publication: United States NLM ID: 7708173 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1546-3141 (Electronic) Linking ISSN: 0361803X NLM ISO Abbreviation: AJR Am J Roentgenol Subsets: MEDLINE |
| أسماء مطبوعة: | Publication: <2004-> : Leesburg, VA : American Roentgen Ray Society Original Publication: Springfield, Ill., Thomas. |
| مواضيع طبية MeSH: | Artificial Intelligence* , Respiratory Function Tests*, Pulmonary Emphysema/*diagnostic imaging , Tomography, X-Ray Computed/*methods, Adult ; Aged ; Aged, 80 and over ; Female ; Humans ; Male ; Middle Aged ; Radiographic Image Interpretation, Computer-Assisted ; Retrospective Studies |
| مستخلص: | OBJECTIVE. The purpose of this study was to evaluate an artificial intelligence (AI)-based prototype algorithm for fully automated quantification of emphysema on chest CT compared with pulmonary function testing (spirometry). MATERIALS AND METHODS. A total of 141 patients (72 women, mean age ± SD of 66.46 ± 9.7 years [range, 23-86 years]; 69 men, mean age of 66.72 ± 11.4 years [range, 27-91 years]) 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. Segmentation of the lung based on two different reconstruction methods was carried out by using a deep convolution image-to-image network. This multilayer convolutional neural network was combined with multilevel feature chaining and depth monitoring. To discriminate the output of the network from ground truth, an adversarial network was used during training. Emphysema was quantified using spatial filtering and attenuation-based thresholds. Emphysema quantification and TI were compared using the Spearman correlation coefficient. RESULTS. The mean TI for all patients was 0.57 ± 0.13. The mean percentages of emphysema using reconstruction methods 1 and 2 were 9.96% ± 11.87% and 8.04% ± 10.32%, respectively. AI-based emphysema quantification showed very strong correlation with TI (reconstruction method 1, ρ = -0.86; reconstruction method 2, ρ = -0.85; both p < 0.0001), indicating that AI-based emphysema quantification meaningfully reflects clinical pulmonary physiology. CONCLUSION. AI-based, fully automated emphysema quantification shows good correlation with TI, potentially contributing to an image-based diagnosis and quantification of emphysema severity. |
| فهرسة مساهمة: | Keywords: CT; artificial intelligence; chronic obstructive pulmonary disease; emphysema quantification; lung function values |
| تواريخ الأحداث: | Date Created: 20200305 Date Completed: 20200624 Latest Revision: 20200624 |
| رمز التحديث: | 20240628 |
| DOI: | 10.2214/AJR.19.21572 |
| PMID: | 32130041 |
| قاعدة البيانات: | MEDLINE |
| تدمد: | 1546-3141 |
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| DOI: | 10.2214/AJR.19.21572 |