Analysis and classification of kidney stones based on Raman spectroscopy.

Xiaoyu Cui,Gejun Zhang,Zeyin Zhao,Jiao Lu,Yue Zhao,Shuo Chen

doi:10.1364/boe.9.004175

Abstract

The number of patients with kidney stones worldwide is increasing, and it is particularly important to facilitate accurate diagnosis methods. Accurate analysis of the type of kidney stones plays a crucial role in the patient's follow-up treatment. This study used microscopic Raman spectroscopy to analyze and classify the different mineral components present in kidney stones. There were several Raman changes observed for the different types of kidney stones and the four types were oxalates, phosphates, purines and L-cystine kidney stones. We then combined machine learning techniques with Raman spectroscopy. KNN and SVM combinations with PCA (PCA-KNN, PCA-SVM) methods were implemented to classify the same spectral data set. The results show the diagnostic accuracies are 96.3% for the PCA-KNN and PCA-SVM methods with high sensitivity (0.963, 0.963) and specificity (0.995,0.985). The experimental Raman spectra results of kidney stones show the proposed method has high classification accuracy. This approach can provide support for physicians making treatment recommendations to patients with kidney stones

Highlights

Kidney stones are a global problem that seriously threatens human health [1]
This study demonstrates the use of Raman spectroscopy combined with machine learning techniques can classify spectral data obtained from different kidney stones
The combination of Raman spectroscopy and statistical tools has great potential for the effective diagnosis and study of kidney stones. This is the first report classifying kidney stones according to Raman spectroscopy combined with machine learning methods

Summary

Introduction

Kidney stones are a global problem that seriously threatens human health [1]. The stones cause physical pain and lead to chronic kidney disease [2]. Recent statistics indicate the incidence of kidney stones is increasing worldwide, and the incidence rate usually varies from 2 to 20% [3,4,5]. The prevalence of kidney stones in adolescents and children is increasing and has become common [6]. Treating kidney stones is a painful process for most patients. Kidney stones are an extremely recurrent disease and survey data show 52% of patients relapse within 10 years [8] and 3% of patients experience renal failure because of urolithiasis [9].preventing recurrence is critical

Methods

Results

Conclusion