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

• Published in: Bioengineering (Basel) Vol. 11; no. 7; p. 675
• 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: Journal Article
• Language: English
• Published: Switzerland MDPI AG 02.07.2024
• Subjects: Accuracy; Algorithms; Artificial intelligence; Artificial neural networks; Biofilms; bitewing radiograph; Deep learning; Dental calculi; Dental calculus; Dentistry; Dentists; Digital imaging; Gum disease; Image databases; Image enhancement; Image filters; Machine learning; medical image; Medical imaging; Neural networks; Oral diseases; Oral hygiene; Teeth; Visual aids; X-rays; YOLOv8; bitewing radiograph; medical image; dental calculus; image enhancement; YOLOv8
• ISSN: 2306-5354; 2306-5354
• DOI: 10.3390/bioengineering11070675

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.