College of Physical Science and Technology, Guangxi Normal University, Guilin, Guangxi 541004, China.
College of Physical Science and Technology, Guangxi Normal University, Guilin, Guangxi 541004, China; Guangxi Key Laboratory of Nuclear Physics and Technology, Guangxi Normal University, 541004, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2025 Feb 5;326:125209. doi: 10.1016/j.saa.2024.125209. Epub 2024 Sep 23.
Alzheimer's disease (AD) and vascular dementia (VaD) typically do not exhibit distinct differences in clinical manifestations and auxiliary examination results, which leads to a high misdiagnosis rate. However, significant differences in treatment approaches and prognosis between these two diseases underscore the critical need for an accurate diagnosis of AD and VaD. In this study, serum samples from 33 patients with AD patients, 37 patients with VaD, and 130 healthy individuals were collected, employing near-infrared aquaphotomics technology in combination with deep learning for differential diagnoses. Through an analysis of water absorption patterns among different diseases via aquaphotomics, the efficacies of traditional machine learning methods (Support Vector Machine and Decision Trees) and deep learning approaches (Deep Forest) in modeling were compared. Ultimately, by leveraging feature extraction techniques in conjunction with deep learning, a differential diagnostic model for AD and VaD was successfully developed. The results revealed that aquaphotomics could identify a certain correlation between the number of hydrogen bonds in water molecules and the development of AD and VaD; the deep learning model was found to be superior to traditional machine learning models, achieving an accuracy of 98.67 %, sensitivity of 97.33 %, and specificity of 100.00 %. The bands identified using the Competitive Adaptive Reweighting Algorithm method, primarily located at approximately 1300-1500 nm, showed a significant correlation with water molecules containing four hydrogen bonds. These results highlighted the potential role of the water molecule hydrogen-bond network in disease development and were consistent with the aquaphotomics analysis results. Therefore, the differential diagnostic model developed by integrating near-infrared spectroscopy and deep learning was proven to be effective and feasible, providing accurate and rapid diagnostic methods for AD and VaD diagnoses.
阿尔茨海默病（AD）和血管性痴呆（VaD）在临床表现和辅助检查结果上通常没有明显差异，导致误诊率较高。但是，这两种疾病的治疗方法和预后存在显著差异，这凸显了准确诊断 AD 和 VaD 的重要性。在这项研究中，采集了 33 例 AD 患者、37 例 VaD 患者和 130 例健康个体的血清样本，采用近红外水敏光学生物分析技术结合深度学习进行鉴别诊断。通过水敏光学生物分析对不同疾病的水分吸收模式进行分析，比较了传统机器学习方法（支持向量机和决策树）和深度学习方法（深林）在建模中的效果。最终，通过结合特征提取技术和深度学习，成功开发了用于 AD 和 VaD 的鉴别诊断模型。结果表明，水敏光学生物分析可以识别水分子中氢键数量与 AD 和 VaD 发展之间的一定相关性；深度学习模型优于传统机器学习模型，准确率为 98.67%，灵敏度为 97.33%，特异性为 100.00%。使用竞争自适应重加权算法方法识别的波段主要位于 1300-1500nm 左右，与含有四个氢键的水分子有显著相关性。这些结果强调了水分子氢键网络在疾病发展中的潜在作用，与水敏光学生物分析结果一致。因此，整合近红外光谱和深度学习的鉴别诊断模型被证明是有效和可行的，为 AD 和 VaD 的诊断提供了准确、快速的诊断方法。
