# Development and Validation of an Optical Sensor-Based Automated Urine Flow Meter for Real-Time Patient Monitoring

## TL;DR

A new automated urine flow meter called P-meter was developed to monitor urine output in real-time, improving early detection of acute kidney injury.

## Contribution

The novel P-meter uses optical sensors and microcontroller automation for precise, low-cost urine output monitoring.

## Key findings

- The P-meter achieved high accuracy with R2 = 0.9889 and minimal bias in its final prototype.
- The device outperformed earlier prototypes in terms of volumetric precision and agreement with reference methods.
- The P-meter offers a cost-effective alternative to commercial systems for clinical urine output monitoring.

## Abstract

Acute kidney injury (AKI) affects thousands of hospitalized patients annually, yet early detection remains challenging as serum creatinine elevation lags behind clinical deterioration. Decreased urine output (UO) represents a key diagnostic criterion of AKI, sometimes manifesting hours before biochemical changes; however, current manual monitoring methods are labor-intensive and prone to error. Here, we developed and validated a simple, cost-effective automated urine flow meter using non-contact optical sensors, a peristaltic pump, and microcontroller-based automation for precise, real-time monitoring of urine output in clinical settings, named P-meter. Three successive prototypes (V1, V2, V3) were validated against gold-standard gravimetric measurements over 285 h of testing during animal experiments that required bladder catheterization. Iterative refinement addressed miniaturization challenges, fluid dynamics optimization, and sensor positioning to achieve progressively improved accuracy. The optimized V3 prototype demonstrated further enhanced volumetric precision, stability, and flow accuracy with near-unity linearity vs. reference method (R2 = 0.9889), minimal bias (mean error −0.1 mL), and 94.18% agreement within confidence limits (n = 86), outperforming the initial V1 prototype (R2 = 0.9971, mean error −1.69 mL, n = 207) and intermediate V2 design (R2 = 0.9941, mean error 3.63 mL, n = 390), primarily in terms of reduced bias and improved agreement. The P-meter offers accurate urine output monitoring at a lower cost than commercial systems, facilitating its use in early AKI detection and thereby improving patient outcomes.