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Recognition of Digital Dental X-ray Images Using a Convolutional Neural Network

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Abstract

Digital dental X-ray images are an important basis for diagnosing dental diseases, especially endodontic and periodontal diseases. Conventional diagnostic methods depend on the experience of doctors, so they are highly subjective and consume more energy than other approaches. The current computer-aided interpretation technology has low accuracy and poor lesion classification. This study proposes an efficient and accurate method for identifying common lesions in digital dental X-ray images by a convolutional neural network (CNN). In total, 188 digital dental X-ray images that were previously diagnosed as periapical periodontitis, dental caries, periapical cysts, and other common dental diseases by dentists in Qilu Hospital of Shandong University were collected and augmented. The images and labels were inputted into four CNN models for training, including visual geometry group (VGG)-16, InceptionV3, residual network (ResNet)-50, and densely connected convolutional networks (DenseNet)-121. The average classification accuracy of the four trained network models on the test set was 95.9%, while the classification accuracy of the trained DenseNet-121 network model reached 99.5%. It is demonstrated that the use of CNNs to interpret digital dental X-ray images is an efficient and accurate way to conduct auxiliary diagnoses of dental diseases.

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Funding

Our research was supported by the National Natural Science Foundation of China (No. 52172282).

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Author notes

  1. Feng Liu and Lei Gao are the co-first authors.

Authors and Affiliations

  1. School of Information Science and Engineering, Shandong University, Qingdao, 266237, People’s Republic of China

    Feng Liu, Jun Wan & Min Han

  2. Department of First Operating Room, Qilu Hospital of Shandong University, Jinan, 250012, People’s Republic of China

    Lei Gao

  3. Department of Oral Radiology, Qilu Hospital of Shandong University, Jinan, 250012, People’s Republic of China

    Zhi-Lei Lyu & Ying-Ying Huang

  4. Department of Oral and Maxillofacial Surgery, Qilu Hospital of Shandong University, Jinan, 250012, People’s Republic of China

    Chao Liu

  5. Department of Oral Surgery, Shanghai Key Laboratory of Stomatology, National Clinical Research Center of Stomatology, Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, People’s Republic of China

    Chao Liu

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  1. Feng Liu
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  2. Lei Gao
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  3. Jun Wan
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  4. Zhi-Lei Lyu
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  5. Ying-Ying Huang
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  6. Chao Liu
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  7. Min Han
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Correspondence to Chao Liu or Min Han.

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Liu, F., Gao, L., Wan, J. et al. Recognition of Digital Dental X-ray Images Using a Convolutional Neural Network. J Digit Imaging 36, 73–79 (2023). https://doi.org/10.1007/s10278-022-00694-9

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  • DOI: https://doi.org/10.1007/s10278-022-00694-9

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