Deep Learning -Based Dental Issue Detection

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Year : 2025 | Volume :16 | Issue : 01 | Page : –
    By

    Priti Bhalekar,

  • Durga Sawant,

  • Shrushti Hapase,

  • Suraj Khamkar,

  • Namrata Patole,

  1. Assistant Professor, Department of Electronics and Telecommunications, SKN Sinhgad Institute of Technology & Science, Maharashtra, India
  2. Assistant Professor, Department of Electronics and Telecommunications, SKN Sinhgad Institute of Technology & Science, Maharashtra, India
  3. Student, Department of Electronics and Telecommunications, SKN Sinhgad Institute of Technology & Science, Maharashtra, India
  4. Assistant Professor, Department of Electronics and Telecommunications, SKN Sinhgad Institute of Technology & Science, Maharashtra, India
  5. Assistant Professor, Department of Electronics and Telecommunications, SKN Sinhgad Institute of Technology & Science, Maharashtra, India

Abstract

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Abstract— Dentistry is vital for preserving oral health, a key component of overall wellness. Early identification of dental issues is crucial for effective treatment and avoiding further complications. Conventional approaches to diagnosing dental problems typically depend on physical examinations and visual assessments by skilled professionals, which can be both time-intensive and influenced by individual judgment.

In recent years, the application of deep learning algorithms has demonstrated significant potential in automating and enhancing the detection of dental problems. These innovative systems are enhancing diagnostic precision while significantly cutting down analysis time, making dental care processes more efficient. An important area for future research involves creating intuitive interfaces that allow these technologies to be easily adopted in dental clinics, catering to practitioners with diverse technical skills. Moreover, there is increasing interest in integrating various imaging techniques, like 3D scans and cone-beam computed tomography (CBCT), to deliver a more thorough evaluation of dental and oral health. The proposed system, built on convolutional neural network (CNN) architecture, is specifically designed to analyze diverse dental images, including X-rays and intraoral photographs, allowing for precise identification of issues such as cavities, periodontal disease, and structural abnormalities. By bridging the gap between cutting-edge technology and practical application, these advancements hold the promise of revolutionizing dental diagnostics and improving patient outcomes.

Keywords: Deep Learning, Dental Health, convolutional neural network (CNN), XGBoost, Random Forest.

[This article belongs to Research & Reviews: A Journal of Dentistry (rrjod)]

How to cite this article:
Priti Bhalekar, Durga Sawant, Shrushti Hapase, Suraj Khamkar, Namrata Patole. Deep Learning -Based Dental Issue Detection. Research & Reviews: A Journal of Dentistry. 2025; 16(01):-.
How to cite this URL:
Priti Bhalekar, Durga Sawant, Shrushti Hapase, Suraj Khamkar, Namrata Patole. Deep Learning -Based Dental Issue Detection. Research & Reviews: A Journal of Dentistry. 2025; 16(01):-. Available from: https://journals.stmjournals.com/rrjod/article=2025/view=0

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Regular Issue Subscription Review Article
Volume 16
Issue 01
Received 14/11/2024
Accepted 22/01/2025
Published 01/02/2025
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