骨关节炎软骨的近红外光谱学研究及分期诊断

骨关节炎是一种威胁中老年人群公共健康和生活质量的重大医学疾病.骨关节炎的早期病变主要表现在细胞外基质成分含量的变化,患者自身很难发现,现有的临床方法和实验方法也不能较准确地识别骨关节炎的早期病变.近年来,傅里叶变换近红外(FTNIR)光谱技术因为其分析速度快、成本低、易于穿透组织获得样本的光谱信息等特点已被用于手术导航...

NIR Spectroscopic Study and Staging Diagnosis of Osteoarthritic Articular Cartilage

Osteoarthritis (OA) is a major medical disease that threatens the middle-aged and aged people’s public health and quality of life. The early lesions of OA are mainly shown in the content changes of extracellular matrix components, which are difficult to detect by patients themselves, even the existing clinical and experimental methods. In recent years, Fourier transform near-infrared (FTNIR) spectroscopy has been used for the field of surgical navigation,non-destructive testing and various disease diagnoses due to its fast speed, low cost, ease of penetrating tissue to obtain spectral information, etc. Based on the above advantages, FTNIR technology was used to collect and analyze NIR spectra of healthy and multi-period OA articular cartilage at different depth zones (superficial zone, transitional zone and deep zone) in this paper. Then, principal component analysis (PCA) and Fisher discrimination algorithm (FDA) were combined for studying the influence of different preprocessing methods on discrimination results, the changes of matrix composition at different zones, and the diagnosis of multi-period OA. Compared to other two preprocessing methods (baseline correction, second-derivative cubic polynomials 25-point Savitzky-Golay smoothing) at same zone, the preprocessing of first-derivative quadratic polynomials 21-point Savitzky-Golay smoothing shows the best discrimination results, and the recognition rates at the superficial zone are as high as 95% and 90% for initial case and cross validation case, respectively. The discrimination results at the superficial zone are better than those at the transitional zone, and far better than those at the deep zone, which proves that the early lesions in OA mainly occurs at superficial zone. In the multi-period OA recognition, the recognition rates of initial case, cross-validation and prediction sets through optimized data by the FDA model are 100%, 93.3%, and 87.5%, respectively. The results indicate that the first derivative pretreatment of the NIR spectra combined with the PCA-FDA method can effectively identify whether the articular cartilage is diseased and which period it is, which is of great significance in study of monitoring OA and early diagnosis. NIR technology with the appropriate spectral analysis method can be applied to the in situ staging and early clinical diagnosis of OA.