Microwave dielectric property based classification of renal calculi: Application of a kNN algorithm

Abstract

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 to 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 nearest neighbors (kNN) machine learning algorithm with the 10 nearest neighbors, where accuracy as high as 98.17% is achieved.Keywords: Dielectric properties of renal calculi, kidney stone, open-ended coaxial probe, Cole-Cole parameters, classification of kidney stones, machine learning, k-nearest neighbors 5 proper medication and dietary restrictions. However, to designate the appropriate prescription, the renal calculi types should be determined by analyzing the discarded material. There are four major renal calculi types: calcium oxalate (CaOx), cystine, struvite, and uric acid. Types of renal calculi can be

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“…In the reported literature, despite the reported high measurement error, different potential applications of the technique have been proposed. An example of a practical application is the utilization of the probe for kidney stone classification [6]. Another practical realization is using the probe for biopsy or surgical margin determination or a biopsy probe [7,8].…”

“…To mitigate the error, several approaches can be applied including hardware and mathematical updates. However, previously reported studies proved that, without costly updates on the method, the accuracy of tissue classification can be increased by adopting machine learning (ML) based classification algorithms [6][7][8]. Such algorithms are not necessarily concerned with determining the dielectric properties of the samples; however, they determine the type of the sample under test.…”

“…Based on the reported classification results in previous studies, the accuracy can be improved approximately 10% to 30% with this approach. The reported accuracy advancements were obtained by application of classification algorithms to two types of datasets the first and mostly used one is the dielectric property data and the second one is the parameters of mathematical models [6][7][8]. The mathematical models are widely used to represent the dispersive dielectric property behavior with respect to frequency.…”