Detecting Pulp Stones with Automatic Deep Learning in Bitewing Radiographs: A Pilot Study of Artificial Intelligence

Authors : Ali ALTINDAĞ, Sultan UZUN, İbrahim Şevki BAYRAKDAR, Özer ÇELİK
Pages : 12-16
Doi:10.52037/eads.2023.0004
View : 27 | Download : 12
Publication Date : 2023-04-30
Article Type : Research Paper
Abstract :Purpose: This study aims to examine the diagnostic performance of detecting pulp stones with a deep learning model on bite-wing radiographs. Material and Methods: 2203 radiographs were scanned retrospectively. 1745 pulp stones were marked on 1269 bite-wing radiographs with the CranioCatch labeling program insert ignore into journalissuearticles values(CranioCatch, Eskişehir, Turkey); in patients over 16 years old after the consensus of two experts of Maxillofacial Radiologists. This dataset was divided into 3 grou as training insert ignore into journalissuearticles values(n = 1017 insert ignore into journalissuearticles values(1396 labels);, validation insert ignore into journalissuearticles values(n = 126 insert ignore into journalissuearticles values(174 labels);); and test insert ignore into journalissuearticles values(n = 126); insert ignore into journalissuearticles values(175 labels); sets, respectively. The deep learning model was developed using Mask R-CNN architecture. A confusion matrix was used to evaluate the success of the model. Results: The results of precision, sensitivity, and F1 obtained using the Mask R-CNN architecture in the test dataset were found to be 0.9115, 0.8879, and 0.8995, respectively. Discussion- Conclusion: Deep learning algorithms can detect pulp stones. With this, clinicians can use software systems based on artificial intelligence as a diagnostic support system. Mask R-CNN architecture can be used for pulp stone detection with approximately 90% sensitivity. The larger data sets increase the accuracy of deep learning systems. More studies are needed to increase the success rates of deep learning models.
Keywords : artificial intelligence, Bitewing radiography, deep learning, pulp stone

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