Advancements in Dental Filling Detection Technologies and Strategies for Comprehensive Oral Health Care

Authors

  • Shivampeta Aparna Gitam University, Hyderabad Campus, India
  • Himabindu Gottumukkala GITAM School of Technology, India
  • Nitya Shivampet Oak Park High School, Thousand Oaks, CA, USA
  • Kireet Muppavaram GITAM School of Technology, India
  • Chaitanya C. V. Ramayanam University of Southern California, Los Angeles, CA, USA
Volume: 14 | Issue: 3 | Pages: 14470-14474 | June 2024 | https://doi.org/10.48084/etasr.7400

Abstract

Many individuals face issues with their teeth, requiring the expertise of dentists to provide necessary care. Despite the advancements in dental techniques, there is a persistent shortage of dentists, prompting the development of tools to help the latter efficiently perform patient treatment. The current research focuses on refining the precision of the vital dental treatment known as dental fillings. The approach involves utilizing the Mask Region-based Convolutional Neural Network (MaskRCNN) with different variants of ResNET, such as ResNET50, ResNET101 C4, Dilated C5, and Feature Pyramid Network (FPN), to analyze diverse dental radiographs. By training on a broad range of tooth images, this methodology creates a pixel-based masking system, improving dentists' ability to precisely identify filling levels. Consequently, this innovation contributes significantly to expediting and refining the accuracy of dental treatments, ultimately benefiting individuals with tooth problems. Additionally, as a future prospect, this model can enable robots to perform dental operations as it provides pixel-level information necessary for the treatment.

Keywords:

dental x-rays, RESNET, MaskRCNN, annotations, dental fillings, FPN, ROI pooling

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How to Cite

[1]
S. Aparna, H. Gottumukkala, N. Shivampet, K. Muppavaram, and C. C. V. Ramayanam, “Advancements in Dental Filling Detection Technologies and Strategies for Comprehensive Oral Health Care”, Eng. Technol. Appl. Sci. Res., vol. 14, no. 3, pp. 14470–14474, Jun. 2024.

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