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. 2020 May 5;10(1):7531.
doi: 10.1038/s41598-020-64509-z.

Deep Learning Hybrid Method to Automatically Diagnose Periodontal Bone Loss and Stage Periodontitis

Affiliations

Deep Learning Hybrid Method to Automatically Diagnose Periodontal Bone Loss and Stage Periodontitis

Hyuk-Joon Chang et al. Sci Rep. .

Abstract

We developed an automatic method for staging periodontitis on dental panoramic radiographs using the deep learning hybrid method. A novel hybrid framework was proposed to automatically detect and classify the periodontal bone loss of each individual tooth. The framework is a hybrid of deep learning architecture for detection and conventional CAD processing for classification. Deep learning was used to detect the radiographic bone level (or the CEJ level) as a simple structure for the whole jaw on panoramic radiographs. Next, the percentage rate analysis of the radiographic bone loss combined the tooth long-axis with the periodontal bone and CEJ levels. Using the percentage rate, we could automatically classify the periodontal bone loss. This classification was used for periodontitis staging according to the new criteria proposed at the 2017 World Workshop on the Classification of Periodontal and Peri-Implant Diseases and Conditions. The Pearson correlation coefficient of the automatic method with the diagnoses by radiologists was 0.73 overall for the whole jaw (p < 0.01), and the intraclass correlation value 0.91 overall for the whole jaw (p < 0.01). The novel hybrid framework that combined deep learning architecture and the conventional CAD approach demonstrated high accuracy and excellent reliability in the automatic diagnosis of periodontal bone loss and staging of periodontitis.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Overall procedure for a hybrid framework of deep learning architecture and the conventional CAD approach to detect and classify periodontal bone loss.
Figure 2
Figure 2
Detection results for the periodontal bone level (ae), the CEJ level (fj), and the teeth and implants (ko) by the developed CNN.
Figure 3
Figure 3
The long-axis orientations of the tooth and the implant (ae), the intersection points of the tooth (implant) long-axis with the periodontal bone level and the CEJ level (fixture top level), the percentage rate of the radiographic bone loss (fj), and the stages of the periodontitis for each tooth and implant (ko) (correctly classified stages in white color, and incorrectly classified stages in orange color).

References

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