Deep learning-based quantitative analysis of dental caries using optical coherence tomography: An ex vivo study

The conventional approach for diagnosing dental caries is clinical examination and supplemented by radiographs. However, studies based on the clinical and radiographic examination methods often show low sensitivity and high specificity. Machine learning and deep learning techniques can be used to enhance optical coherence tomography (OCT) to more accurately identify diseased and damaged tissue. In this paper, we present a novel approach combining OCT imaging modality and deep convolutional neural network (CNN) for the detection of occlusal carious lesions. A total of 51 extracted human permanent teeth were collected and categorized into three groups: Non-carious teeth, caries extending into enamel, and caries extending into dentin. In data acquisition and ex-vivo OCT imaging, the samples were imaged using spectral-domain OCT system operating at 1300nm center wavelength with a scan rate of 5.5-76kHz, and axial resolution of 5.5μm in air. To acquire images with minimum inhomogeneity, imaging was performed multiple times at different points. For deep learning, OCT images of extracted human carious and non-carious teeth were input to a CNN classifier to determine variations in tissue densities resembling the demineralization process. The CNN model employs two convolutional and pooling layers to extract features and then classify each patch based on the probabilities from the SoftMax classification layer. The sensitivity and specificity of distinguishing between carious and non-carious lesions were found to be 98% and 100%, respectively. This proposed deep learning-based OCT method can reliably classify the oral tissues with various densities, and could be extremely valuable in early dental caries detection.