Auxiliary Diagnosis of Dental Calculus Based on Deep Learning and Image Enhancement by Bitewing Radiographs

• Main 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
• Format: text
• Published: Archīum Ateneo 2024
• Subjects: bitewing radiograph; dental calculus; image enhancement; medical image; YOLOv8; Analytical, Diagnostic and Therapeutic Techniques and Equipment; Biomedical; Biomedical Engineering and Bioengineering; Computational Engineering; Diagnosis; Electrical and Computer Engineering; Engineering; Medicine and Health Sciences
• Online Access: https://archium.ateneo.edu/intelligent-visual-env/4; https://archium.ateneo.edu/context/intelligent-visual-env/article/1003/viewcontent/bioengineering_11_00675_v2.pdf
• Institution: Ateneo De Manila University

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.