Detection of Proximal Caries Lesions with Deep Learning Algorithm

Author: Hyuntae KIM ¹ ; Ji-Soo SONG ; Teo Jeon SHIN ; Hong-Keun HYUN ; Jung-Wook KIM ; Ki-Taeg JANG ; Young-Jae KIM
Author Information

1. Department of Pediatric Dentistry, School of Dentistry, Seoul National University
Publication Type:1
From: Journal of Korean Academy of Pediatric Dentistry 2022;49(2):131-139
CountryRepublic of Korea
Language:English
Abstract: This study aimed to evaluate the effectiveness of deep convolutional neural networks (CNNs) for diagnosis of interproximal caries in pediatric intraoral radiographs. A total of 500 intraoral radiographic images of first and second primary molars were used for the study. A CNN model (Resnet 50) was applied for the detection of proximal caries. The diagnostic accuracy, sensitivity, specificity, receiver operating characteristic (ROC) curve, and area under ROC curve (AUC) were calculated on the test dataset. The diagnostic accuracy was 0.84, sensitivity was 0.74, and specificity was 0.94. The trained CNN algorithm achieved AUC of 0.86. The diagnostic CNN model for pediatric intraoral radiographs showed good performance with high accuracy. Deep learning can assist dentists in diagnosis of proximal caries lesions in pediatric intraoral radiographs.