A computer vision enhanced smart phone platform for microfluidic urine glucometry
A computer vision enhanced smart phone platform for microfluidic urine glucometry
复制标题
DOI：
10.1039/d3an01356a
复制
发表时间：
2023-12-08
期刊：
影响因子：
4.2
通讯作者：
Javanmard，Mehdi
中科院分区：
文献类型：
--
作者：
Meng，Zhuolun;Tayyab，Muhammad;Javanmard，Mehdi
葡萄糖是诊断和预防各种疾病的重要生物标志物，包括糖尿病和低血糖症，这些疾病可能产生严重的副作用，症状，甚至导致患者死亡。因此，需要快速且经济的葡萄糖水平测量来帮助识别处于潜在风险中的那些人。随着智能手机用户的增加，便携式智能手机葡萄糖传感器正在变得流行。在本文中，我们提出了一种一次性微流控葡萄糖传感器，准确，快速地量化人体尿液中的葡萄糖水平，使用比色分析和计算机视觉相结合。该葡萄糖传感器实现了一种一次性微流体设备，该设备基于载玻片上的医用级胶带和葡萄糖分析条，集成了定制的聚二甲基硅氧烷（PDMS）微型泵，可加速毛细流动，使其经济、方便、快速且无需设备。在吸收目标溶液后，一次性设备被滑入3D打印的主底盘，并仅用发光二极管（LED）照明，这是颜色敏感实验的关键。采集图像后，将图像导入算法，以使用计算机视觉和平均RGB值测量来测量葡萄糖水平。这篇文章说明了葡萄糖传感器在定量蔗糖水中葡萄糖时令人印象深刻的准确性和一致性。传感器使用的计算机视觉方法与生物学领域的传统测量方法之间的密切一致性以及不同传感器性能之间观察到的微小变化证明了这一点。研究中使用的指数回归曲线进一步证实了葡萄糖浓度和平均RGB值之间的强相关性，R平方值为0.997，表明这些变量之间的高度相关性。文章还强调了所描述的解决方案对其他类型的测定和基于智能手机的传感器的潜在可转移性。
Glucose is an important biomarker for diagnosing and prognosing various diseases, including diabetes and hypoglycemia, which can have severe side effects, symptoms, and even lead to death in patients. As a result, there is a need for quick and economical glucose level measurements to help identify those at potential risk. With the increase in smartphone users, portable smartphone glucose sensors are becoming popular. In this paper, we present a disposable microfluidic glucose sensor that accurately and rapidly quantifies glucose levels in human urine using a combination of colorimetric analysis and computer vision. This glucose sensor implements a disposable microfluidic device based on medical-grade tapes and glucose analysis strips on a glass slide integrated with a custom-made polydimethylsiloxane (PDMS) micropump that accelerates capillary flow, making it economical, convenient, rapid, and equipment-free. After absorbing the target solution, the disposable device is slid into the 3D-printed main chassis and illuminated exclusively with Light Emitting Diode (LED) illumination, which is pivotal to color-sensitive experiments. After collecting images, the images are imported into the algorithm to measure the glucose levels using computer vision and average RGB values measurements. This article illustrates the impressive accuracy and consistency of the glucose sensor in quantifying glucose in sucrose water. This is evidenced by the close agreement between the computer vision method used by the sensor and the traditional method of measuring in the biology field, as well as the small variation observed between different sensor performances. The exponential regression curve used in the study further confirms the strong relationship between glucose concentrations and average RGB values, with an R-square value of 0.997 indicating a high degree of correlation between these variables. The article also emphasizes the potential transferability of the solution described to other types of assays and smartphone-based sensors.