Efficacy of a deep leaning model created with the transfer learning method in detecting sialoliths of the submandibular gland on panoramic radiography

Objective: This study aimed to compare the performance of 3 deep learning models, including a model constructed with the transfer learning method, in detecting submandibular gland sialoliths on panoramic radiographs.

Study design: We used data from 2 institutions (A and B) to create the models for use in institution B. In total, 224 panoramic radiographs with sialoliths were used. Model 1 was created using data from institution A only, model 2 was created using combined data from institutions A and B, and model 3 was created using the transfer learning method by having model 1 transferred and trained in various learning epochs using data from institution B. These models were tested and compared in their detection performance using testing data sets from institution B.

Results: Model 2 and model 3 with 300 epochs performed equally well and yielded the highest detection rates (recall: sensitivity of 85%, precision: positive predictive value of 100%, and F measure of 91.9%) for sialoliths on panoramic radiographs.

Conclusion: The results of this study suggest that use of the transfer learning method with an appropriate number of epochs may be an alternative to sharing patient personal data among institutions.