Development of a Portable Multi-Sensor Urine Test and Data Collection Platform for Risk Assessment of Kidney Stone Formation

Abstract

In this paper, we present an Internet of things (IoT)-based data collection system for the risk assessment of urinary stone formation, or urolithiasis, by the measurement and storage of four parameters in urine: pH, concentrations of ionized calcium (Ca2+), uric acid and total dissolved solids. The measurements collected by the system from patients and healthy individuals grouped by age and gender will be stored in a cloud database. These will be used in the training phase of an artificial intelligence (AI) machine learning process utilizing the logistics regression model. The trained model provides a binary risk assessment, indicating if the end user is either a stone-former or not. For system validation, standard chemical solutions were used. Preliminary results indicated a sufficient measurement range, falling within the physiological range, and resolution for pH (2.0–10.0, +/−0.1), Ca2+(0.1–3.0 mmol/l, +/−0.05), uric acid (20–500 ppm, +/−1) and conductivity (1.0–40.0 mS/cm, +/−0.1), exhibiting high correlation with standard instruments. We intend to deploy this system in few hospitals in Taiwan to collect the data of patients’ urine, with analysis aided by urologist assessments for the risk of urolithiasis. The modularized design allows future modification and expansion to accommodate other sensing analytes.

Predictive System Implementation to Improve the Accuracy of Urine Self-Diagnosis with Smartphones: Application of a Confusion Matrix-Based Learning Model through RGB Semiquantitative Analysis

“…Therefore, it is useful to provide a standard for quantitatively predicting the degree of a suspected disease, which can be presented in the same way as the test result in a hospital, thereby increasing the reliability of the examiner. In the existing urinalysis app, color interpolation is applied for accuracy and an analysis method using linear interpolation was applied [ 37 ], but this requires that all RGB values are applied. In addition, the app has its own standard algorithm which digitally extracts the color table provided by the urine dipstick company; therefore, there may be differences depending on the app [ 38 ].…”

Electronic Solutions for Artificial Intelligence Healthcare

“…Wen-Yaw Chung, et al suggested the "Development of a Portable Multi-Sensor Urine Test and Data Collection Platform for Risk Assessment of Kidney Stone Formation" [24]. The suggested platform is based on the Internet of things (IoT) data collection system.…”

“…There are a total of twenty-five papers, including review papers and research papers. This Special Issue consists of seven review papers [2][3][4][5][6][7][8] and eighteen research papers [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26].…”