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- Title
Auxiliary Diagnosis of Dental Calculus Based on Deep Learning and Image Enhancement by Bitewing Radiographs.
- Authors
Lin, Tai-Jung; Lin, Yen-Ting; Lin, Yuan-Jin; Tseng, Ai-Yun; Lin, Chien-Yu; Lo, Li-Ting; Chen, Tsung-Yi; Chen, Shih-Lun; Chen, Chiung-An; Li, Kuo-Chen; Abu, Patricia Angela R.
- Abstract
In the field of dentistry, the presence of dental calculus is a commonly encountered issue. If not addressed promptly, it has the potential to lead to gum inflammation and eventual tooth loss. Bitewing (BW) images play a crucial role by providing a comprehensive visual representation of the tooth structure, allowing dentists to examine hard-to-reach areas with precision during clinical assessments. This visual aid significantly aids in the early detection of calculus, facilitating timely interventions and improving overall outcomes for patients. This study introduces a system designed for the detection of dental calculus in BW images, leveraging the power of YOLOv8 to identify individual teeth accurately. This system boasts an impressive precision rate of 97.48%, a recall (sensitivity) of 96.81%, and a specificity rate of 98.25%. Furthermore, this study introduces a novel approach to enhancing interdental edges through an advanced image-enhancement algorithm. This algorithm combines the use of a median filter and bilateral filter to refine the accuracy of convolutional neural networks in classifying dental calculus. Before image enhancement, the accuracy achieved using GoogLeNet stands at 75.00%, which significantly improves to 96.11% post-enhancement. These results hold the potential for streamlining dental consultations, enhancing the overall efficiency of dental services.
- Subjects
DENTAL calculus; IMAGE intensifiers; CONVOLUTIONAL neural networks; TOOTH loss; DENTAL care
- Publication
Bioengineering (Basel), 2024, Vol 11, Issue 7, p675
- ISSN
2306-5354
- Publication type
Article
- DOI
10.3390/bioengineering11070675