State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.
School of Public Health, Xinjiang Medical University, Urumqi, China.
J Biophotonics. 2023 Aug;16(8):e202200354. doi: 10.1002/jbio.202200354. Epub 2023 May 21.
While cholecystitis is a critical public health problem, the conventional diagnostic methods for its detection are time consuming, expensive and insufficiently sensitive. This study examined the possibility of using serum fluorescence spectroscopy and machine learning for the rapid and accurate identification of patients with cholecystitis. Significant differences were observed between the fluorescence spectral intensities of the serum of cholecystitis patients (n = 74) serum and those of healthy subjects (n = 71) at 455, 480, 485, 515, 625 and 690 nm. The ratios of characteristic fluorescence spectral peak intensities were first calculated, and principal component analysis (PCA)-linear discriminant analysis (LDA) and PCA-support vector machine (SVM) classification models were then constructed using the ratios as variables. Compared with the PCA-LDA model, the PCA-SVM model displayed better diagnostic performance in differentiating cholecystitis patients from healthy subjects, with an overall accuracy of 96.55%. This exploratory study showed that serum fluorescence spectroscopy combined with the PCA-SVM algorithm has significant potential for the development of a rapid cholecystitis screening method.
虽然胆囊炎是一个严重的公共卫生问题，但目前用于胆囊炎检测的常规诊断方法耗时、昂贵且灵敏度不足。本研究探讨了利用血清荧光光谱和机器学习快速准确识别胆囊炎患者的可能性。在 455nm、480nm、485nm、515nm、625nm 和 690nm 处，胆囊炎患者（n=74）和健康对照者（n=71）血清的荧光光谱强度存在显著差异。首先计算特征荧光光谱峰强度的比值，然后使用比值作为变量构建主成分分析（PCA）-线性判别分析（LDA）和 PCA-支持向量机（SVM）分类模型。与 PCA-LDA 模型相比，PCA-SVM 模型在区分胆囊炎患者和健康对照者方面表现出更好的诊断性能，总准确率为 96.55%。这项探索性研究表明，血清荧光光谱结合 PCA-SVM 算法在开发快速胆囊炎筛查方法方面具有很大的潜力。
