Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2020 Jan;26(1):152-158.
doi: 10.1111/odi.13223. Epub 2019 Nov 18.

Diagnosis of cystic lesions using panoramic and cone beam computed tomographic images based on deep learning neural network

Jae-Hong Lee  1 , Do-Hyung Kim  1 , Seong-Nyum Jeong  1
Affiliations

Diagnosis of cystic lesions using panoramic and cone beam computed tomographic images based on deep learning neural network

Jae-Hong Lee et al. Oral Dis. 2020 Jan.

Abstract

Objectives: The aim of the current study was to evaluate the detection and diagnosis of three types of odontogenic cystic lesions (OCLs)-odontogenic keratocysts, dentigerous cysts, and periapical cysts-using dental panoramic radiography and cone beam computed tomographic (CBCT) images based on a deep convolutional neural network (CNN).

Methods: The GoogLeNet Inception-v3 architecture was used to enhance the overall performance of the detection and diagnosis of OCLs based on transfer learning. Diagnostic indices (area under the ROC curve [AUC], sensitivity, specificity, and confusion matrix with and without normalization) were calculated and compared between pretrained models using panoramic and CBCT images.

Results: The pretrained model using CBCT images showed good diagnostic performance (AUC = 0.914, sensitivity = 96.1%, specificity = 77.1%), which was significantly greater than that achieved by other models using panoramic images (AUC = 0.847, sensitivity = 88.2%, specificity = 77.0%) (p = .014).

Conclusions: This study demonstrated that panoramic and CBCT image datasets, comprising three types of odontogenic OCLs, are effectively detected and diagnosed based on the deep CNN architecture. In particular, we found that the deep CNN architecture trained with CBCT images achieved higher diagnostic performance than that trained with panoramic images.

Keywords: cysts; deep learning; odontogenic cysts; supervised machine learning.

PubMed Disclaimer

References

REFERENCES

    1. Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., & Zheng, X. (2016). TensorFlow: Large-scale machine learning on heterogeneous distributed systems. arXiv e-print: arXiv:1603.04467.
    1. Binnie, W. H. (1999). Periodontal cysts and epulides. Periodontology 2000, 21, 16-32.
    1. Borghesi, A., Nardi, C., Giannitto, C., Tironi, A., Maroldi, R., Di Bartolomeo, F., & Preda, L. (2018). Odontogenic keratocyst: Imaging features of a benign lesion with an aggressive behaviour. Insights into Imaging, 9(5), 883-897. https://doi.org/10.1007/s13244-018-0644-z
    1. Chollet, F. (2017). Keras. Retrieved from https://github.com/fchollet/keras
    1. Dunfee, B. L., Sakai, O., Pistey, R., & Gohel, A. (2006). Radiologic and pathologic characteristics of benign and malignant lesions of the mandible. Radiographics, 26(6), 1751-1768. https://doi.org/10.1148/rg.266055189

Grants and funding

LinkOut - more resources