期刊
DIAGNOSTICS
卷 11, 期 2, 页码 -出版社
MDPI
DOI: 10.3390/diagnostics11020250
关键词
machine learning; deep learning; artificial intelligence; paranasal sinusitis; multi-view radiographs
资金
- National Research Foundation of Korea [NRF-2015R1C1A1A02037475, NRF-2018R1C1B6007917]
- SNUBH Research Fund [02-2017-029]
A deep learning algorithm was developed for diagnosing sinusitis on both Waters' and Caldwell views, showing comparable diagnostic performance to radiologists. The algorithm can detect and classify frontal, ethmoid, and maxillary sinusitis without manual cropping, enhancing the value of radiography in assessing multiple sinusitis.
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 (n = 1403, sinusitis% = 34.3%) and the test set (n = 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 (p = 0.012 and 0.013, respectively). The multi-view model also exhibited a higher AUC than the single Waters' view model for maxillary sinusitis (p = 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.
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