Detecting anaemia in resource-limited settings is challenging due to limited access to diagnostic tools, healthcare infrastructure, and trained personnel, often resulting in underdiagnosis and delayed treatment. This study aims to evaluate HemoQR, a novel point-of-care (POC) diagnostic method for haemoglobin (Hb) level detection using a smartphone-based system integrated with a paper strip without needing any reagent or additional auxiliary device. The goal is to assess the efficacy of this portable, user-friendly, rapid and accurate alternative for Hb measurement in large-scale intervention programmes such as Anaemia-mukt Bharat (AMB), which is crucial for diagnosing anaemia and monitoring various health conditions. The method involves the transfer of one drop of the blood sample to a paper strip that is made indigenously using plant based cellulosic sources for optimal functionality. The colour signal around the test spot is quantitatively analysed using a smartphone camera equipped with a custom application. The application processes the images through a machine learning algorithm to determine the Hb concentration. The system was evaluated in a clinical trial involving 200 participants, comparing its performance with the gold-standard laboratory Hb assay using automated haematology analyser. The smartphone-based method demonstrated high sensitivity (87.09 %) and specificity (96.11 %) for anaemia screening, as compared with laboratory results. The average detection time was significantly reduced to under q minute, and the system showed excellent user-friendliness and portability with no auxiliary device needed beyond the smartphone. Clinical trial results indicated that the device was accurate and reliable across different Hb levels. Summarily, HemoQR evidenced to be a promising tool for POC diagnostics. Its high accuracy, rapid results, and ease of use make it suitable for both clinical settings and remote areas with limited access to laboratory facilities. Future research will prioritize further validation and integration of the anaemia screening outcome with electronic health records from a large population cohort. This will help identify early indicators and signals of numerous emerging disease burdens that could escalate into secondary and tertiary health crises if not addressed in time.

中文翻译：

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使用植物来源的“HemoQR”纸条和智能手机进行快速无试剂贫血筛查：一项对 200 名人类受试者的研究


由于诊断工具、医疗保健基础设施和训练有素的人员有限，因此在资源有限的环境中检测贫血具有挑战性，这通常会导致诊断不足和延误治疗。本研究旨在评估 HemoQR，这是一种用于血红蛋白 （Hb） 水平检测的新型即时 （POC） 诊断方法，使用基于智能手机的系统与纸条集成，无需任何试剂或额外的辅助设备。目标是评估这种便携式、用户友好、快速和准确的 Hb 测量替代方案在大规模干预计划（如贫血 - mukt Bharat （AMB））中的疗效，这对于诊断贫血和监测各种健康状况至关重要。该方法涉及将一滴血样转移到纸条上，该纸条是使用植物纤维素来源本土制造的，以实现最佳功能。使用配备自定义应用程序的智能手机摄像头对测试点周围的颜色信号进行定量分析。该应用程序通过机器学习算法处理图像以确定 Hb 浓度。该系统在一项涉及 200 名参与者的临床试验中进行了评估，将其性能与使用自动血液学分析仪的金标准实验室 Hb 测定进行比较。与实验室结果相比，基于智能手机的方法对贫血筛查表现出高灵敏度 （87.09%） 和特异性 （96.11%）。平均检测时间显著缩短至 q 分钟以下，并且该系统表现出出色的用户友好性和便携性，除了智能手机之外不需要辅助设备。临床试验结果表明，该设备在不同 Hb 水平下都是准确可靠的。 总而言之，HemoQR 被证明是一种很有前途的 POC 诊断工具。其准确性高、结果快速且易于使用，使其适用于临床环境和无法使用实验室设施的偏远地区。未来的研究将优先考虑进一步验证和整合贫血筛查结果与来自大型人群队列的电子健康记录。这将有助于识别许多新出现的疾病负担的早期指标和信号，如果不及时解决，这些负担可能会升级为继发性和三级健康危机。