Advances in Multiclass Oral Cancer Detection Using Spectroscopic and AI Techniques

Year : 2025 | Volume : 16 | Issue : 03 | Page : 39 48
    By

    Dipali Wankhade,

  • Shailesh Gahane,

  • Mrunal Meshram,

  1. Research Scholar, Datta Meghe Institute of Higher Education and Research Wardha, Maharashtra, India
  2. Researcher, Datta Meghe Institute of Higher Education and Research, (Declared as Deemed-to-be-University), Wardha, Maharashtra, India
  3. Associate Professor, Dept. of Oral Medicine & Radiology, Sharad Pawar Dental Collage, Sawangi, Wardha, Maharashtra, India

Abstract

Oral cancer, primarily OSCC, is still a major health issue worldwide, especially in low-HDI countries. Early diagnosis is essential since survival rates for early detection are much higher than for late-stage detection. However, traditional methods like visual inspection and biopsy are time-consuming, invasive, and rely on the clinician’s skill, which is a limitation in accessibility and efficiency. Oral cancer detection has just been revolutionized by recent advances in spectroscopic techniques, Raman, fluorescence, and infrared spectroscopy coupled with artificial intelligence. These non-invasive methods allow for the analysis of real-time tissue capturing molecular changes relevant to cancer progression. AI enhances diagnostic accuracy through pattern recognition, feature extraction, and multiclass classification of oral lesions over difficulties created by lesion heterogeneity. Deep learning models like CNNs and machine learning techniques like SVMs show high sensitivity and specificity for distinguishing normal, precancerous, and malignant tissues. Fluorescence imaging and gold nanoparticles also seem to hold potential in the bettering of cancer diagnostics. Yet, issues of dataset diversity, model interpretability, and computational demands are there. This review presents the transformative possibility of spectroscopy-AI integration in advancing the diagnosis of oral cancer, bridging the present gaps and avenues for future advancement.

Keywords: Oral cancer, spectroscopy, artificial intelligence, machine learning, deep learning, Raman spectroscopy, fluorescence spectroscopy, infrared spectroscopy, multiclass classification, early diagnosis, non-invasive diagnostics.

[This article belongs to Research and Reviews: A Journal of Dentistry ]

How to cite this article:
Dipali Wankhade, Shailesh Gahane, Mrunal Meshram. Advances in Multiclass Oral Cancer Detection Using Spectroscopic and AI Techniques. Research and Reviews: A Journal of Dentistry. 2025; 16(03):39-48.
How to cite this URL:
Dipali Wankhade, Shailesh Gahane, Mrunal Meshram. Advances in Multiclass Oral Cancer Detection Using Spectroscopic and AI Techniques. Research and Reviews: A Journal of Dentistry. 2025; 16(03):39-48. Available from: https://journals.stmjournals.com/rrjod/article=2025/view=209549


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Regular Issue Subscription Review Article
Volume 16
Issue 03
Received 26/04/2025
Accepted 04/05/2025
Published 05/05/2025
Publication Time 9 Days
141

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