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

Journal: Lasers in surgery and medicine
PMID: 40197749

Abstract

OBJECTIVES: Oral squamous cell carcinoma (OSCC) is the sixth-highest incidence of malignant tumors worldwide. However, early diagnosis is complex owing to the impracticality of biopsying every potentially premalignant intraoral lesion. Here, we present a sub-diffuse reflectance spectroscopy combined with a machine learning method for oral mucosal disease identification. This method provides a noninvasive cost-effective identification option for early signs of malignancy.

Authors

  • Limin Zhang
    School of Information, University of Arizona, 1103 E. Second Street, Tucson, AZ 85705, USA.
  • Qing Chang
    Department of Ultrasound, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
  • Qi Zhang
    Department of Gastroenterology, The Affiliated Hospital of Qingdao University, Qingdao, China.
  • Siyi Zou
    College of Precision Instrument and Optoelectronics Engineering, Tianjin University, Tianjin, China.
  • Dongyuan Liu
    College of Precision Instrument and Optoelectronics Engineering, Tianjin University, Tianjin, China.
  • Feng Gao
    Department of Statistics, UCLA, Los Angeles, CA 90095, USA.
  • Chenlu Liu
    Department of Oral Medicine, Tianjin Stomatological Hospital, School of Medicine, Nankai University, Tianjin, China.

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