Scholarlink

<p>This research introduces a cost-effective and portable urine test strip reader tailored for point-of-care diagnostics. By leveraging computer vision and image analysis, the device quantifies colorimetric changes on urine reagent strips, mitigating the limitations of visual interpretation in traditional dipstick methods. The system is built using a Raspberry Pi 4B and a high-definition webcam, measuring the concentrations of 10 crucial urinalysis parameters. It captures images of the test strips at specific intervals, extracting RGB color values from the test patch regions. Calibration curves correlating RGB values with analyte concentrations over time were developed, achieving an accuracy of 88.7% in determining unknown samples. Detailed analysis of the individual R, G, and B channels provided insights into the color development process. Costing under INR 10,000 and operating on battery power, the reader is an affordable, portable alternative to expensive commercial analyzers, ideal for use in remote areas with limited diagnostic lab access. This device meets the need for quantitative urinalysis in resource-constrained environments and has the potential to enhance healthcare delivery, disease management, and urine biomarker research.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Machine Vision-Powered Point-of-Care Diagnostics: A Quantitative Analysis of Urine Test Strips

  • 期刊论文

  • 发表日期：

    2026年5月26日

查看全文
  • Prachi Bhoir,
  • Tejal Shinde,
  • Jagannath Nirmal,
  • Ninad Mehendale

摘要

This research introduces a cost-effective and portable urine test strip reader tailored for point-of-care diagnostics. By leveraging computer vision and image analysis, the device quantifies colorimetric changes on urine reagent strips, mitigating the limitations of visual interpretation in traditional dipstick methods. The system is built using a Raspberry Pi 4B and a high-definition webcam, measuring the concentrations of 10 crucial urinalysis parameters. It captures images of the test strips at specific intervals, extracting RGB color values from the test patch regions. Calibration curves correlating RGB values with analyte concentrations over time were developed, achieving an accuracy of 88.7% in determining unknown samples. Detailed analysis of the individual R, G, and B channels provided insights into the color development process. Costing under INR 10,000 and operating on battery power, the reader is an affordable, portable alternative to expensive commercial analyzers, ideal for use in remote areas with limited diagnostic lab access. This device meets the need for quantitative urinalysis in resource-constrained environments and has the potential to enhance healthcare delivery, disease management, and urine biomarker research.