Utility of a Deep Learning Algorithm for Detection of Reticular Opacity on Chest Radiograph in Patients with Interstitial Lung Disease.

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Background: Deep learning has been heavily explored for pulmonary nodule detection on chest radiograph. Reticular opacity detection in interstitial lung disease (ILD) is challenging and may also benefit from a deep learning algorithm (DLA). Objective: To evaluate the utility of DLA for detection of reticular opacity on chest radiographs in patients with surgically confirmed ILD. Methods: This retrospective study included 197 patients (130 men, 67 women; mean age 62.6±7.6 years) with surgically proven ILD between January 2017 and December 2018 who underwent preoperative chest radiograph and chest CT within a 30-day interval. A total of 197 age- and sex-matched control patients with normal chest radiographs were randomly selected. Commercially available DLA was used to detect lower lobe or subpleural abnormalities; those matching reticular opacity location on CT were deemed true-positives. Six readers (three thoracic radiologists; three residents) independently reviewed radiographs for reticular opacity presence with and without DLA. Interobserver agreement was assessed. Diagnostic performance was compared among interpretations. Subanalysis was performed according to CT-based classification of reticular opacity severity. DLA performance was also assessed in 102 chest radiographs from a different institution (51 with ILD; 51 matched controls). Results: Interobserver agreement was moderate (κ=0.517) for readers alone versus almost perfect (κ=0.870) for readers with DLA. Sensitivity, specificity, and accuracy for reticular opacity for DLA were 98.0%, 99.0%, and 98.5%; for pooled readers alone were 77.3%, 92.3%, and 84.8%; and for readers with DLA were 93.8%, 97.3%, and 95.6%. All metrics were significantly better (all p≤.002) for DLA and for readers with DLA compared with readers alone. Sensitivity for readers without and with DLA were 66.7% and 86.8% in mild disease, 84.2% and 98.8% in moderate disease, and 87.3% and 100.0% in severe disease. DLA exhibited 100.0% accuracy in the cases from the second center. Conclusions: DLA outperformed readers in reticular opacity detection, and use of DLA improved reader performance and interobservser agreement. Benefit of DLA was more notable in terms of sensitivity than specificity and was maintained in mild disease. Clinical Impact: Use of DLA may facilitate detection of reticular opacity on chest radiograph in the early stages of ILD.