Home Dental Radiology Performance comparison of three deep learning models for impacted mesiodens detection on periapical radiographs

Performance comparison of three deep learning models for impacted mesiodens detection on periapical radiographs

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  • White, S. C. & Pharoah, M. J. Oral Radiology-E-Book: Principles and Interpretation (Elsevier Health Sciences, 2014).


    Google Scholar
     

  • Asaumi, J. I. et al. Radiographic examination of mesiodens and their associated complications. Dentomaxillofac. Radiol. 33, 125–127. https://doi.org/10.1259/dmfr/68039278 (2004).

    CAS 
    PubMed 

    Google Scholar
     

  • Russell, K. A. & Folwarczna, M. A. Mesiodens—Diagnosis and management of a common supernumerary tooth. J. Can. Dent. Assoc. 69, 362–366 (2003).

    PubMed 

    Google Scholar
     

  • Gündüz, K., Çelenk, P., Zengin, Z. & Sümer, P. Mesiodens: A radiographic study in children. J. Oral Sci. 50, 287–291 (2008).

    PubMed 

    Google Scholar
     

  • Sha, X. et al. Comparison between periapical radiography and cone beam computed tomography for the diagnosis of anterior maxillary trauma in children and adolescents. Dent. Traumatol. 38, 62–70. https://doi.org/10.1111/edt.12706 (2022).

    PubMed 

    Google Scholar
     

  • American Dental Association Council on Scientific Affairs. Dental radiographic examinations: Recommendations for patient selection and limiting radiation exposure. https://www.fda.gov/media/84818/download (2012).

  • An, S.-Y., Lee, K.-M. & Lee, J.-S. Korean dentists’ perceptions and attitudes regarding radiation safety and protection. Dentomaxillofac. Radiol. 47, 20170228 (2018).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lee, A. et al. Deep learning neural networks to differentiate Stafne’s bone cavity from pathological radiolucent lesions of the mandible in heterogeneous panoramic radiography. PLoS One 16, e0254997 (2021).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lee, J.-H., Han, S.-S., Kim, Y. H., Lee, C. & Kim, I. Application of a fully deep convolutional neural network to the automation of tooth segmentation on panoramic radiographs. Oral Surg. Oral Med. Oral Pathol. Oral Radiol. 129, 635–642 (2020).

    PubMed 

    Google Scholar
     

  • Kim, Y. H. et al. Automated cortical thickness measurement of the mandibular condyle head on CBCT images using a deep learning method. Sci. Rep. 11, 1–11 (2021).

    ADS 

    Google Scholar
     

  • Lee, J. H., Kim, D. H. & Jeong, S. N. Diagnosis of cystic lesions using panoramic and cone beam computed tomographic images based on deep learning neural network. Oral Dis. 26, 152–158 (2020).

    PubMed 

    Google Scholar
     

  • Lee, C. T. et al. Use of the deep learning approach to measure alveolar bone level. J. Clin. Periodontol. https://doi.org/10.1111/jcpe.13574 (2021).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Chen, H. et al. A deep learning approach to automatic teeth detection and numbering based on object detection in dental periapical films. Sci. Rep. 9, 1–11 (2019).

    ADS 

    Google Scholar
     

  • Lee, J.-H., Kim, D.-H., Jeong, S.-N. & Choi, S.-H. Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. J. Dent. 77, 106–111 (2018).

    PubMed 

    Google Scholar
     

  • Ha, E.-G., Jeon, K. J., Kim, Y. H., Kim, J.-Y. & Han, S.-S. Automatic detection of mesiodens on panoramic radiographs using artificial intelligence. Sci. Rep. 11, 1–8 (2021).


    Google Scholar
     

  • Kwon, O. et al. Automatic diagnosis for cysts and tumors of both jaws on panoramic radiographs using a deep convolution neural network. Dentomaxillofac. Radiol. 49, 20200185 (2020).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Nawaz, M. et al. An efficient deep learning approach to automatic glaucoma detection using optic disc and optic cup localization. Sensors 22, 434 (2022).

    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Goyal, M. & Hassanpour, S. A refined deep learning architecture for diabetic foot ulcers detection. Preprint at arXiv:2007.07922 (2020).

  • Redmon, J. & Farhadi, A. Yolov3: An incremental improvement. Preprint at arXiv:1804.02767 (2018).

  • Liu, C., Hu, S.-C., Wang, C., Lafata, K. & Yin, F.-F. Automatic detection of pulmonary nodules on CT images with YOLOv3: Development and evaluation using simulated and patient data. Quant. Imaging Med. Surg. 10, 1917 (2020).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Takahashi, T., Nozaki, K., Gonda, T., Mameno, T. & Ikebe, K. Deep learning-based detection of dental prostheses and restorations. Sci. Rep. 11, 1–7 (2021).


    Google Scholar
     

  • Takahashi, T. et al. Identification of dental implants using deep learning—Pilot study. Int. J. Implant Dent. 6, 1–6 (2020).


    Google Scholar
     

  • Lin, T.-Y., Goyal, P., Girshick, R., He, K. & Dollár, P. Focal loss for dense object detection. In Proceedings of the IEEE International Conference on Computer Vision 2980–2988 (2017).

  • Tan, M., Pang, R. & Le, Q. V. Efficientdet: Scalable and efficient object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 10781–10790 (2020).

  • Everingham, M., Van Gool, L., Williams, C. K., Winn, J. & Zisserman, A. The pascal visual object classes (VOC) challenge. Int. J. Comput. Vis. 88, 303–338 (2010).


    Google Scholar
     

  • Kise, Y. et al. Preliminary study on the application of deep learning system to diagnosis of Sjögren’s syndrome on CT images. Dentomaxillofac. Radiol. 48, 20190019 (2019).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Liang, K., Zhang, L., Yang, Y., Yang, H. & Xing, Y. A self-supervised deep learning network for low-dose CT reconstruction. In 2018 IEEE Nuclear Science Symposium and Medical Imaging Conference Proceedings (NSS/MIC) 1–4 (IEEE, 2018).

  • Kuwada, C. et al. Deep learning systems for detecting and classifying the presence of impacted supernumerary teeth in the maxillary incisor region on panoramic radiographs. Oral Surg. Oral Med. Oral Pathol. Oral Radiol. 130, 464–469 (2020).

    PubMed 

    Google Scholar
     

  • Pang, S. et al. A novel YOLOv3-arch model for identifying cholelithiasis and classifying gallstones on CT images. PLoS One 14, e0217647 (2019).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Harsono, I. W., Liawatimena, S. & Cenggoro, T. W. Lung nodule detection and classification from thorax CT-scan using retinanet with transfer learning. J. King Saud Univ. Comput. Inf. Sci. (2020).

  • Kim, D., Choi, J., Ahn, S. & Park, E. A smart home dental care system: Integration of deep learning, image sensors, and mobile controller. J. Ambient Intell. Humaniz. Comput. https://doi.org/10.1007/s12652-021-03366-8 (2021).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Cao, L. et al. The field wheat count based on the EfficientDet algorithm. In 2020 IEEE 3rd International Conference on Information Systems and Computer Aided Education (ICISCAE) 557–561 (IEEE, 2020).

  • Song, S., Jing, J., Huang, Y. & Shi, M. EfficientDet for fabric defect detection based on edge computing. J. Eng. Fibers Fabr. 16, 15589250211008346 (2021).


    Google Scholar
     

  • Gautam, A. & Singh, S. Neural style transfer combined with EfficientDet for thermal surveillance. Vis. Comput., 1–17 (2021).

  • Talukdar, K., Bora, K., Mahanta, L. B. & Das, A. K. A comparative assessment of deep object detection models for blood smear analysis. Tissue Cell 76, 101761 (2022).

    CAS 
    PubMed 

    Google Scholar
     



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