Universitat Autònoma de Barcelona, Centre Grup de Tècniques de Separació en Química (GTS), Unitat de Química Analítica, Departament de Química, 08193 Bellaterra, Spain.
J Biomed Opt. 2012 Jul;17(7):076027. doi: 10.1117/1.JBO.17.7.076027.
The formation of kidney stones is a common and highly studied disease, which causes intense pain and presents a high recidivism. In order to find the causes of this problem, the characterization of the main compounds is of great importance. In this sense, the analysis of the composition and structure of the stone can give key information about the urine parameters during the crystal growth. But the usual methods employed are slow, analyst dependent and the information obtained is poor. In the present work, the near infrared (NIR)-hyperspectral imaging technique was used for the analysis of 215 samples of kidney stones, including the main types usually found and their mixtures. The NIR reflectance spectra of the analyzed stones showed significant differences that were used for their classification. To do so, a method was created by the use of artificial neural networks, which showed a probability higher than 90% for right classification of the stones. The promising results, robust methodology, and the fast analytical process, without the need of an expert assistance, lead to an easy implementation at the clinical laboratories, offering the urologist a rapid diagnosis that shall contribute to minimize urolithiasis recidivism.
肾结石的形成是一种常见且研究深入的疾病，它会引起剧烈疼痛，并呈现出高复发率。为了找到这个问题的原因，对主要化合物的特征描述非常重要。从这个意义上说，对结石组成和结构的分析可以提供有关晶体生长过程中尿液参数的关键信息。但是，常用的方法耗时、依赖分析人员，并且获得的信息质量较差。在本工作中，近红外（NIR）高光谱成像技术用于分析 215 个肾结石样本，包括通常发现的主要类型及其混合物。分析后的结石的近红外反射光谱显示出明显的差异，可用于对其进行分类。为此，创建了一种使用人工神经网络的方法，该方法对结石的正确分类的概率高于 90%。有前景的结果、稳健的方法和快速的分析过程，无需专家协助，这使得它可以在临床实验室中轻松实施，为泌尿科医生提供快速诊断，有助于降低尿石症的复发率。
