Oral submucous fibrosis (OSF) is a precancerous condition which leads to the development of fibrous band and loss of oral mucosa elasticity. In the absence of medical treatment OSF can also lead to oral cancer. In the present work, in vivo fluorescence data obtained from the oral tissues of the pre-treated OSF subjects, post-treated OSF subjects, and the normal healthy volunteers were subjected to principal component analysis (PCA) and partial least square discriminate analysis (PLS-DA). PCA and PLS-DA are unsupervised and supervised pattern recognition methods, respectively. PCA and PLS-DA models were found to be highly sensitive and specific. The receiver operating characteristic (ROC) curve of the PLS-DA models mainly consisted of horizontal and vertical lines with an area under the curve of greater than 0.9 which shows that PLS-DA models are genuine classifier. Outcomes of the present work clearly illustrate that by combining the in vivo fluorescence spectroscopy with PCA and PLS-DA, a fast, sensitive, and non-invasive procedure could be obtained for the diagnosis of OSF affected subjects. The developed diagnosis procedure could be of significant importance in rural areas where access to clinical diagnosis is relatively difficult and costly.
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