Department of Electronics and Communication Engineering, Istanbul Technical University, Istanbul, Turkey.
Department of Electronics and Communication Engineering, Istanbul Technical University, Istanbul, Turkey.
Comput Biol Med. 2019 Sep;112:103366. doi: 10.1016/j.compbiomed.2019.103366. Epub 2019 Jul 23.
The proper management of renal lithiasis presents a challenge, with the recurrence rate of the disease being as high as 46%. To prevent recurrence, the first step is the accurate categorization of the discarded renal calculi. Currently, the discarded renal calculi type is determined with the X-ray powder diffraction method which requires a cumbersome sample preparation. This work presents a new approach that can enable fast and accurate classification of discarded renal calculi with minimal sample preparation requirements. To do so, first, the measurements of the dielectric properties of naturally formed renal calculi are collected with the open-ended contact probe technique between 500 MHz and 6 GHz with 100 MHz intervals. Cole-Cole parameters are fitted to the measured dielectric properties with the generalized Newton-Raphson method. The renal calculi types are classified based on their Cole-Cole parameters as calcium oxalate, cystine, or struvite. The classification is performed using k-nearest neighbors (kNN) machine learning algorithm with the 10 nearest neighbors, where accuracy as high as 98.17% is achieved.
肾结石的合理管理具有挑战性，该疾病的复发率高达 46%。为了防止复发，第一步是准确分类丢弃的肾结石。目前，丢弃的肾结石类型是用 X 射线粉末衍射法确定的，这种方法需要繁琐的样品制备。本工作提出了一种新方法，只需最小的样品制备要求，即可快速准确地分类丢弃的肾结石。为此，首先使用开口接触探头技术在 500 MHz 至 6 GHz 之间以 100 MHz 的间隔收集天然形成的肾结石的介电特性测量值。用广义牛顿-拉普森法对测量的介电特性进行 Cole-Cole 参数拟合。根据 Cole-Cole 参数将肾结石类型分类为草酸钙、胱氨酸或鸟粪石。分类使用 k-最近邻（kNN）机器学习算法和 10 个最近邻进行，准确率高达 98.17%。
