Classification of Approximal Caries in Bitewing Radiographs Using Convolutional Neural Networks

Abstract

Dental caries is an extremely common problem in dentistry that affects a significant part of the population. Approximal caries are especially difficult to identify because their position makes clinical analysis difficult. Radiographic evaluation—more specifically, bitewing images—are mostly used in such cases. However, incorrect interpretations may interfere with the diagnostic process. To aid dentists in caries evaluation, computational methods and tools can be used. In this work, we propose a new method that combines image processing techniques and convolutional neural networks to identify approximal dental caries in bitewing radiographic images and classify them according to lesion severity. For this study, we acquired 112 bitewing radiographs. From these exams, we extracted individual tooth images from each exam, applied a data augmentation process, and used the resulting images to train CNN classification models. The tooth images were previously labeled by experts to denote the defined classes. We evaluated classification models based on the Inception and ResNet architectures using three different learning rates: 0.1, 0.01, and 0.001. The training process included 2000 iterations, and the best results were achieved by the Inception model with a 0.001 learning rate, whose accuracy on the test set was 73.3%. The results can be considered promising and suggest that the proposed method could be used to assist dentists in the evaluation of bitewing images, and the definition of lesion severity and appropriate treatments.

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“…Initially, CNNs were proposed for image classification problems [ 67 ]. However, currently, CNNs have different types of applications such as object detection, image segmentation, synthetic image generation, among other types of applications in the medical field [ 68 , 69 ].…”

Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis

“…Their goal is to enhance the efficiency and quality of delivered services. AI implementations are invisible algorithms in software tools in the majority of these processes [ 5 , 6 , 31 , 32 , 33 , 34 , 35 , 36 , 37 ]. They often overlap various dental specialties and categorizations.…”

“…They often overlap various dental specialties and categorizations. Currently, they mostly include: AI in X-ray and other diagnostics, caries [ 29 , 30 , 31 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 ] AI in implant dentistry [ 26 , 33 , 45 , 46 ] AI in photography analysis [ 27 , 28 , 29 , 47 ] AI in practice management, tele-dentistry, patient coaching [ 44 , 48 , 49 , 50 , 51 , 52 , 53 , 54 ] AI in clinical predictions (virtual simulation, aging, growth) [ 5 , 55 , 56 , 57 , 58 , 59 ] …”

“…AI in X-ray and other diagnostics, caries [ 29 , 30 , 31 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 ]…”

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