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

IF 2.2 3区医学 Q2 DERMATOLOGY

Lasers in Surgery and Medicine Pub Date : 2025-04-08 DOI:10.1002/lsm.70011

Limin Zhang, Qing Chang, Qi Zhang, Siyi Zou, Dongyuan Liu, Feng Gao, Chenlu Liu

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引用次数: 0

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.

Methods

Sub-diffuse spectra of three oral sites (hypoglottis, buccal, and gingiva) from healthy subjects and three types of mucosal lesions (oral lichen planus, OLP, oral leukoplakia, OLK, and OSCC) from patients were collected by using a home-made sub-diffuse reflectance spectroscopy prototype system, and three features including spectra ratio (SR), first-order derivative(DE) of the spectra and optical parameters (OP) were derived from the original spectra to enhance the insights into the optical properties of the oral mucosal tissues. To accurately classify the spectral features, a support vector machine (SVM) and probabilistic neural network (PNN) were employed.

Result

Most of the statistical distributions of the spectral features demonstrated obvious differences and the two classification methods exhibited comparable performances. For the classification in the oral sites of healthy subjects, the OP-based classification results were unsatisfactory, while the classification results utilizing DR, SR, and DE achieved a least accuracy of 0.8289, sensitivity of 0.8495, sensitivity of 0.9311, and Matthews correlation coefficient of 0.8085. Comparatively, the classification results between OLP, OLK, OSCC, and normal tissue obtained achieved high indexes even using the OP feature.

Conclusion

Integrating sub-diffuse reflectance spectroscopy measurement and suitable machine learning methods can obtain remarkable precision in differentiating different sites of oral mucosa and identifying different types of oral mucosal diseases, especially based on DE features. It is of great help in detecting OSCC and is expected to be a highly sensitive, time-sensitive, and accurate method for oral disease detection.

查看原文本刊更多论文

亚漫反射光谱与机器学习相结合的口腔黏膜疾病识别方法。

目的：口腔鳞状细胞癌（OSCC）是世界上发病率第6高的恶性肿瘤。然而，早期诊断是复杂的，因为对每一个潜在的恶性病变进行活检是不现实的。在这里，我们提出了一种结合机器学习的亚漫反射光谱方法用于口腔粘膜疾病的识别。这种方法为恶性肿瘤的早期症状提供了一种非侵入性的、经济有效的鉴别选择。方法:采用自制的亚漫反射光谱原型系统，采集健康受试者口腔3个部位（声门下炎、颊炎和牙龈）和患者3种类型粘膜病变（口腔扁平苔藓、OLP、口腔白斑、OLK和OSCC）的亚漫反射光谱，包括光谱比（SR）、在原始光谱的基础上推导光谱的一阶导数（DE）和光学参数（OP），以增强对口腔粘膜组织光学特性的认识。为了对光谱特征进行准确分类，采用了支持向量机（SVM）和概率神经网络（PNN）。结果：大部分光谱特征的统计分布存在明显差异，两种分类方法具有可比性。对于健康受试者口腔部位的分类，基于op的分类结果不理想，而使用DR、SR和DE的分类结果准确率最低，为0.8289，灵敏度为0.8495，灵敏度为0.9311，马修斯相关系数为0.8085。相比之下，即使使用OP特征，OLP、OLK、OSCC与正常组织之间的分类结果也获得了较高的指标。结论：将亚漫反射光谱测量与合适的机器学习方法相结合，在区分口腔黏膜不同部位和识别不同类型口腔黏膜疾病方面具有显著的精度，尤其是基于DE特征的口腔黏膜疾病。它对口腔粘膜癌的检测有很大的帮助，有望成为一种高灵敏度、高时敏、高准确度的口腔疾病检测方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Lasers in Surgery and Medicine 医学-外科

CiteScore

5.40

自引率

12.50%

发文量

119

审稿时长

1 months

期刊介绍： Lasers in Surgery and Medicine publishes the highest quality research and clinical manuscripts in areas relating to the use of lasers in medicine and biology. The journal publishes basic and clinical studies on the therapeutic and diagnostic use of lasers in all the surgical and medical specialties. Contributions regarding clinical trials, new therapeutic techniques or instrumentation, laser biophysics and bioengineering, photobiology and photochemistry, outcomes research, cost-effectiveness, and other aspects of biomedicine are welcome. Using a process of rigorous yet rapid review of submitted manuscripts, findings of high scientific and medical interest are published with a minimum delay.