Introduction: Carpal Tunnel Syndrome (CTS) is a prevalent neuropathy requiring accurate, non-invasive diagnostics to minimize patient burden. This study evaluates the New Energy Vision (NEV) camera, an RGB-based multispectral imaging tool, to detect CTS through skin texture and color analysis, developing a machine learning algorithm to distinguish CTS-affected hands from controls. Methods: A two-part observational study included 103 participants (50 controls, 53 CTS patients) in Part 1, using NEV camera images to train a Support Vector Machine (SVM) classifier. Part 2 compared median nerve-damaged (MED) and ulnar nerve-normal (ULN) palm areas in 32 CTS patients. Validations included nerve conduction tests (NCT), Semmes-Weinstein monofilament testing (SWMT), and Boston Carpal Tunnel Questionnaire (BCTQ). Results: The SVM classifier achieved 93.33% accuracy (confusion matrix: [[14, 1], [1, 14]]), with 81.79% cross-validation accuracy. Part 2 identified significant differences (p < 0.05) in color proportions (e.g., red_proportion) and Haralick texture features between MED and ULN areas, corroborated by BCTQ and SWMT. Conclusions: The NEV camera, leveraging multispectral imaging, offers a promising non-invasive CTS diagnostic tool using detection of nerve-related skin changes. Further validation is needed for clinical adoption.

Keywords: BCTQ; Carpal Tunnel Syndrome; Haralick texture features; NEV camera; machine learning; multispectral imaging; non-invasive diagnosis; ultra-weak photon emission.

介绍：腕管综合症（CTS）是一种常见的神经病变，需要准确且无创的诊断方法以减少患者的负担。本研究评估了新能量视觉（NEV）相机，这是一种基于RGB的多光谱成像工具，通过皮肤纹理和颜色分析来检测CTS，并开发了一种机器学习算法，用于区分受影响的手与对照组。方法：这项观察性研究分为两部分，第一部分包括103名参与者（50名对照组，53名CTS患者），使用NEV相机图像训练支持向量机（SVM）分类器。第二部分在32名CTS患者中比较了正中神经损伤区域（MED）和尺神经正常区域（ULN）。验证方法包括神经传导测试（NCT）、Semmes-Weinstein单丝测试（SWMT）以及波士顿腕管综合症问卷（BCTQ）。结果：SVM分类器实现了93.33%的准确率（混淆矩阵：[[14, 1], [1, 14]]），交叉验证准确性为81.79%。第二部分在MED和ULN区域之间发现了颜色比例（例如，红比例）和Haralick纹理特征等方面的显著差异，并且这些结果得到了BCTQ和SWMT的支持。结论：NEV相机利用多光谱成像技术提供了一种有前景的无创CTS诊断工具，通过检测与神经相关的皮肤变化。需要进一步验证以用于临床应用。

关键词：BCTQ；腕管综合症；Haralick纹理特征；NEV相机；机器学习；多光谱成像；非侵入性诊断；超弱光子发射。