Department of Mechanical Engineering, Indian Institute of Technology Kharagpur, Kharagpur 721302, West Bengal, India.
ACS Sens. 2022 Jul 22;7(7):2028-2036. doi: 10.1021/acssensors.2c00806. Epub 2022 Jul 8.
Screening of anemic patients poses demanding challenges in extreme point-of-care settings where the gold standard diagnostic technologies are not pragmatic and the alternative point-of-care technologies suffer from compromised accuracy, prohibitive cost, process complexity, or reagent stability issues. As a disruption to this paradigm, here, we report the development of a smartphone-based sensor for rapid screening of anemic patients by exploiting the patterns formed by a spreading drop of blood on a wet paper strip wherein blood attempts to displace a more viscous fluid, on the porous matrix of a paper, leading to "finger-like" projections at the interface. We analyze the topological features of the pattern via smartphone-enabled image analytics and map the same with the relative occupancy of the red blood cells in the blood sample, allowing for label-free screening and classification of blood samples corresponding to moderate to severe anemic conditions. The accuracy of detection is verified by comparing with gold standard reports of hematology analyzer, showing a strong correlation coefficient () of 0.975. This technique is likely to provide a crucial decision-making tool that obviates delicate reagents and skilled technicians for supreme functionality in resource-limited settings.
在极端的即时护理环境中，对贫血患者进行筛查带来了严峻的挑战，因为黄金标准诊断技术并不实用，而替代即时护理技术则存在准确性降低、成本过高、过程复杂或试剂稳定性等问题。在此，我们报告了一种基于智能手机的传感器的开发，该传感器利用血液在湿纸条上扩散形成的图案来快速筛查贫血患者，其中血液试图在多孔纸基质上取代一种更粘稠的流体，从而在界面处形成“手指状”突起。我们通过智能手机支持的图像分析来分析图案的拓扑特征，并将其与血液样本中红细胞的相对占有率进行映射，从而实现对中重度贫血条件下的血液样本进行无标记筛查和分类。通过与血液分析仪的金标准报告进行比较，验证了检测的准确性，显示出 0.975 的强相关系数 ()。这项技术很可能提供一个关键的决策工具，在资源有限的环境中省去了精密的试剂和熟练的技术人员，实现了卓越的功能。
