Caries detection with tooth surface segmentation on intraoral photographic images using deep learning

• Published in: BMC oral health Vol. 22; no. 1; pp. 573 - 9
• Main Authors: Park, Eun Young, Cho, Hyeonrae, Kang, Sohee, Jeong, Sungmoon, Kim, Eun-Kyong
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 07.12.2022; BioMed Central; BMC
• Subjects: Accuracy; Algorithms; Analysis; Artificial Intelligence; Cameras; Care and treatment; Caries localisation; Classification; Clinics; Convolutional neural network; Datasets; Deep Learning; Dental caries; Dentin; Dentistry; Dentists; Diagnosis; Health aspects; Humans; Image processing; Intraoral camera; Machine learning; Methods; Neural networks; Personal computers; Prospective Studies; Segmentation; Smartphones; Teeth; Tooth surface segmentation; South Korea; Deep learning; Tooth surface segmentation; Intraoral camera; Artificial intelligence; Convolutional neural network; Caries localisation
• ISSN: 1472-6831; 1472-6831
• DOI: 10.1186/s12903-022-02589-1

Intraoral photographic images are helpful in the clinical diagnosis of caries. Moreover, the application of artificial intelligence to these images has been attempted consistently. This study aimed to evaluate a deep learning algorithm for caries detection through the segmentation of the tooth surface using these images. In this prospective study, 2348 in-house intraoral photographic images were collected from 445 participants using a professional intraoral camera at a dental clinic in a university medical centre from October 2020 to December 2021. Images were randomly assigned to training (1638), validation (410), and test (300) datasets. For image segmentation of the tooth surface, classification, and localisation of caries, convolutional neural networks (CNN), namely U-Net, ResNet-18, and Faster R-CNN, were applied. For the classification algorithm for caries images, the accuracy and area under the receiver operating characteristic curve were improved to 0.813 and 0.837 from 0.758 to 0.731, respectively, through segmentation of the tooth surface using CNN. Localisation algorithm for carious lesions after segmentation of the tooth area also showed improved performance. For example, sensitivity and average precision improved from 0.890 to 0.889 to 0.865 and 0.868, respectively. The deep learning model with segmentation of the tooth surface is promising for caries detection on photographic images from an intraoral camera. This may be an aided diagnostic method for caries with the advantages of being time and cost-saving.