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

BMC Oral Health

Research

Eun-Kyong Kim¹  Sohee Kang²  Eun Young Park²  Sungmoon Jeong³  Hyeonrae Cho⁴ 

[1] Department of Dental Hygiene, College of Science and Technology, Kyungpook National University, 2559 Gyeongsangde-ro, Sangju, Gyeongsangbuk-do, South Korea;Department of Dentistry, College of Medicine, Yeungnam University, Daegu, South Korea;Research Center for Artificial Intelligence in Medicine, Kyungpook National University Hospital, Daegu, South Korea;Department of Medical Informatics, School of Medicine, Kyungpook National University, Daegu, South Korea;Research Center for Artificial Intelligence in Medicine, Kyungpook National University Hospital, Daegu, South Korea;School of Electronics Engineering, College of IT Engineering, Kyungpook National University, Daegu, South Korea;

关键词: Artificial intelligence;    Caries localisation;    Convolutional neural network;    Deep learning;    Intraoral camera;    Tooth surface segmentation;   

DOI  :  10.1186/s12903-022-02589-1

 received in 2022-04-26, accepted in 2022-11-14,  发布年份 2022

来源: Springer

【 摘 要 】

BackgroundIntraoral 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.MethodsIn 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.ResultsFor 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.ConclusionThe 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.

【 授权许可】

CC BY   
© The Author(s) 2022

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