Dual Key-Parameter Sensing System Development for Urolithiasis Recurrence Prevention

Chung, Wen-Yaw; Ramezani, Roozbeh Falah; Cheng, Cheanyeh; Wu, Chien-Hua; Ger, Tzong Rong; Hsiung, Nathan S. K.; Wang, Szu-Han; Tsai, Vincent F.S.

doi:10.1145/3397391.3397417

Cited by 1 publication

(4 citation statements)

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“…Figure 1 shows the on-board implementation from [11], used for the measurement of urine sample conductivity. The on-board implementation of the potentiometric circuit presented in [18] was used for measuring the pH and Ca 2+ in urine samples, as shown in Figure 2. The on-board prototype of the potentiometric readout system [18].…”

Section: Methodsmentioning

confidence: 99%

“…The on-board implementation of the potentiometric circuit presented in [18] was used for measuring the pH and Ca 2+ in urine samples, as shown in Figure 2. The on-board prototype of the potentiometric readout system [18].…”

Section: Methodsmentioning

confidence: 99%

“…A motherboard with proper sockets and connectors was used for the assembly of the system and connecting different modules to each other, as shown in Figure 3. The on-board implementation of the potentiometric circuit presented in [18] was used for measuring the pH and Ca 2+ in urine samples, as shown in Figure 2.…”

Section: Methodsmentioning

confidence: 99%

“…A motherboard with proper sockets and connectors was used for the assembly of the system and connecting different modules to each other, as shown in Figure 3. The on-board prototype of the potentiometric readout system [18].…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2020

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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