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http://dx.doi.org/10.5933/JKAPD.2022.49.2.131

Detection of Proximal Caries Lesions with Deep Learning Algorithm  

Hyuntae, Kim (Department of Pediatric Dentistry, School of Dentistry, Seoul National University)
Ji-Soo, Song (Department of Pediatric Dentistry, School of Dentistry, Seoul National University)
Teo Jeon, Shin (Department of Pediatric Dentistry, School of Dentistry, Seoul National University)
Hong-Keun, Hyun (Department of Pediatric Dentistry, School of Dentistry, Seoul National University)
Jung-Wook, Kim (Department of Pediatric Dentistry, School of Dentistry, Seoul National University)
Ki-Taeg, Jang (Department of Pediatric Dentistry, School of Dentistry, Seoul National University)
Young-Jae, Kim (Department of Pediatric Dentistry, School of Dentistry, Seoul National University)
Publication Information
Journal of the korean academy of Pediatric Dentistry / v.49, no.2, 2022 , pp. 131-139 More about this Journal
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.
Keywords
Artificial intelligence; Deep learning; Proximal caries; Primary teeth; Intraoral radiography;
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Times Cited By KSCI : 3  (Citation Analysis)
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