Automated estimation of total lung volume using chest radiographs and deep learning. Issue 7 (18th April 2022)

• Record Type: Journal Article
• Authors: Sogancioglu, Ecem; Murphy, Keelin; Th.Scholten, Ernst; Boulogne, Luuk H.; Prokop, Mathias; van Ginneken, Bram
• Is Part Of: Medical physics. Volume 49:Issue 7(2022)
• Page Start: 4466
• Page End: 4477
• Publication Date: 2022-04-18
• Subjects: artificial intelligence -- chest radiograph -- chest x‐ray -- deep learning -- total lung volume; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.15655
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract: Background: Total lung volume is an important quantitative biomarker and is used for the assessment of restrictive lung diseases. Purpose: In this study, we investigate the performance of several deep‐learning approaches for automated measurement of total lung volume from chest radiographs. Methods: About 7621 posteroanterior and lateral view chest radiographs (CXR) were collected from patients with chest CT available. Similarly, 928 CXR studies were chosen from patients with pulmonary function test (PFT) results. The reference total lung volume was calculated from lung segmentation on CT or PFT data, respectively. This dataset was used to train deep‐learning architectures to predict total lung volume from chest radiographs. The experiments were constructed in a stepwise fashion with increasing complexity to demonstrate the effect of training with CT‐derived labels only and the sources of error. The optimal models were tested on 291 CXR studies with reference lung volume obtained from PFT. Mean absolute error (MAE), mean absolute percentage error (MAPE), and Pearson correlation coefficient (Pearson's r ) were computed. Results: The optimal deep‐learning regression model showed an MAE of 408 ml and an MAPE of 8.1% using both frontal and lateral chest radiographs as input. The predictions were highly correlated with the reference standard (Pearson's r = 0.92). CT‐derived labels were useful for pretraining but the optimal performance was obtained by fine‐tuning the network with PFT‐derived labels. Conclusion: We demonstrate, for the first time, that state‐of‐the‐art deep‐learning solutions can accurately measure total lung volume from plain chest radiographs. The proposed model is made publicly available and can be used to obtain total lung volume from routinely acquired chest radiographs at no additional cost. This deep‐learning system can be a useful tool to identify trends over time in patients referred regularly for chest X‐ray.