Background and Objective

Due to the existing prevalence of nonalcoholic fatty liver disease (NAFLD) and its relation to the epidemic of obesity in the general population, it is imperative to develop detection and evaluation methods of the early stages of the disease with improved efficacy over the current diagnostic approaches. We aimed to obtain an improved diagnosis, combining methods of optical spectroscopy -diffuse reflectance and fluorescence- with statistical data analysis applied to detect early stages of NAFLD.

Methods

Statistical analysis scheme based on quadratic discriminant analysis followed by canonical discriminant analysis were applied to the diffuse reflectance data combined with endogenous fluorescence spectral data excited at one of these wavelengths: 330, 365, 385, 405 or 415 nm. The statistical scheme was also applied to the combinations of fluorescence spectrum (405 nm) with each one of the other fluorescence spectra. Details of the developed software, including the application of machine learning algorithms to the combination of spectral data followed by classification statistical schemes, are discussed.

Results

Steatosis progression was differentiated with little classification error (≤1.3%) by using diffuse reflectance and endogenous fluorescence at different wavelengths. Similar results were obtained using fluorescence at 405 nm and one of the other fluorescence spectra (classification error ≤1.0%). Adding the corresponding areas under the curves to the above combinations of spectra diminished errors to 0.6% and 0.3% or less, respectively. The best results for the compounded reflectance-plus-fluorescence spectra were obtained with fluorescence spectra excited at 415 nm with a total classification error of 0.2%; for the combination of the 405nm-excited fluorescence spectrum with another fluorescence spectrum, the best results were achieved for 385 nm, for which total relative classification error amounted 0.4%. The consideration of the area under the spectral curves further improved both classifiers, reducing the error to 0.0% in both cases.

Conclusion

Spectrometric techniques combined with statistical processing are a promising tool to improve steatosis classification through a label free approach. However, statistical schemes here applied, might result complex for the everyday medical practice, the designed software including machine learning algorithms is able to render automatic classification of samples according to their steatosis grade with low error.

中文翻译：

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光谱学和人工智能相结合的方法评估实验性非酒精性脂肪肝

背景与目的

由于非酒精性脂肪肝疾病（NAFLD）的普遍存在及其与普通人群肥胖病的关系，因此有必要开发一种疾病早期阶段的检测和评估方法，以改善目前的诊断方法的疗效。 。我们旨在获得更好的诊断，将光谱学方法（漫反射和荧光）与统计数据分析相结合，以检测NAFLD的早期阶段。

方法

将基于二次判别分析然后进行正则判别分析的统计分析方案应用于与在以下波长之一激发的内源荧光光谱数据结合的漫反射数据：330、365、385、405或415 nm。统计方案还应用于荧光光谱（405 nm）与其他荧光光谱中的每一个的组合。讨论了所开发软件的详细信息，包括将机器学习算法应用于光谱数据的组合，然后进行分类统计方案。

结果

通过使用不同波长的漫反射和内源荧光，可以区分出脂肪变性的进展，几乎没有分类错误（≤1.3％）。使用405 nm的荧光和其他荧光光谱之一（分类误差≤1.0％）获得了相似的结果。将曲线下的相应区域添加到上述光谱组合中，可将误差分别减少到0.6％和0.3％或更小。在415 nm激发的荧光光谱的总分类误差为0.2％的情况下，获得了复合反射加荧光光谱的最佳结果。对于405nm激发的荧光光谱与另一个荧光光谱的组合，在385 nm处获得了最佳结果，其总相对分类误差为0.4％。

结论

光谱技术与统计处理相结合是通过无标签方法改善脂肪变性分类的一种有前途的工具。但是，此处应用的统计方案可能会导致日常医疗实践变得复杂，包括机器学习算法在内的设计软件能够根据样品的脂肪变性等级对样品进行自动分类，且误差很小。