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Sub-diffuse Reflectance Spectroscopy Combined With Machine Learning Method for Oral Mucosal Disease Identification.

Limin Zhang1, Qing Chang1, Qi Zhang1

  • 1College of Precision Instrument and Optoelectronics Engineering, Tianjin University, Tianjin, China.

Lasers in Surgery and Medicine
|April 8, 2025
PubMed
Summary

This study introduces a noninvasive method using sub-diffuse reflectance spectroscopy and machine learning for early oral cancer detection. The technique accurately identifies oral mucosal diseases, offering a promising tool for timely diagnosis.

Keywords:
feature classificationoral mucosal diseasesprobabilistic neural networksub‐diffuse reflectance spectroscopysupport vector machine

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Area of Science:

  • Biomedical Optics
  • Machine Learning in Healthcare
  • Oral Pathology

Background:

  • Oral squamous cell carcinoma (OSCC) is a globally prevalent malignancy.
  • Early diagnosis of OSCC is challenging due to the difficulty of biopsying all potentially premalignant lesions.
  • Noninvasive diagnostic methods are crucial for improving early detection rates.

Purpose of the Study:

  • To develop and evaluate a noninvasive method for oral mucosal disease identification.
  • To combine sub-diffuse reflectance spectroscopy with machine learning for enhanced diagnostic capabilities.
  • To provide a cost-effective tool for the early detection of malignancy in oral tissues.

Main Methods:

  • Collected sub-diffuse reflectance spectra from healthy oral sites and various mucosal lesions (OLP, OLK, OSCC).
  • Utilized a home-made sub-diffuse reflectance spectroscopy prototype system.
  • Derived spectral features (SR, DE, OP) and employed machine learning algorithms (SVM, PNN) for classification.

Main Results:

  • Spectral features showed significant differences across various oral sites and conditions.
  • Machine learning models achieved high accuracy in classifying oral mucosal diseases.
  • The derivative (DE) feature demonstrated particular effectiveness in differentiating between normal tissue and lesions, including OSCC.

Conclusions:

  • Integrating sub-diffuse reflectance spectroscopy with machine learning provides precise differentiation of oral mucosal sites and diseases.
  • The DE feature is highly valuable for accurate oral disease detection, especially for OSCC.
  • This approach offers a sensitive, timely, and accurate method for oral disease diagnosis.