Deep Learning for Diagnosis of Paranasal Sinusitis Using Multi-View Radiographs

Accurate image interpretation of Waters' and Caldwell view radiographs used for sinusitis screening is challenging. Therefore, we developed a deep learning algorithm for diagnosing frontal, ethmoid, and maxillary sinusitis on both Waters' and Caldwell views. The datasets were selected for the training and validation set ( = 1403, sinusitis% = 34.3%) and the test set ( = 132, sinusitis% = 29.5%) by temporal separation. The algorithm can simultaneously detect and classify each paranasal sinus using both Waters' and Caldwell views without manual cropping. Single- and multi-view models were compared. Our proposed algorithm satisfactorily diagnosed frontal, ethmoid, and maxillary sinusitis on both Waters' and Caldwell views (area under the curve (AUC), 0.71 (95% confidence interval, 0.62-0.80), 0.78 (0.72-0.85), and 0.88 (0.84-0.92), respectively). The one-sided DeLong's test was used to compare the AUCs, and the Obuchowski-Rockette model was used to pool the AUCs of the radiologists. The algorithm yielded a higher AUC than radiologists for ethmoid and maxillary sinusitis ( = 0.012 and 0.013, respectively). The multi-view model also exhibited a higher AUC than the single Waters' view model for maxillary sinusitis ( = 0.038). Therefore, our algorithm showed diagnostic performances comparable to radiologists and enhanced the value of radiography as a first-line imaging modality in assessing multiple sinusitis.