Sickle cell disease (SCD) is a worldwide hematological disorder causing painful episodes, anemia, organ damage, stroke, and even deaths. It is more common in sub-Saharan Africa and other resource-limited countries. Conventional laboratory-based diagnostic methods for SCD are time-consuming, complex, and cannot be performed at point-of-care (POC) and home settings. Optical microscope-based classification and counting demands a significant amount of time, extensive setup, and cost along with the skilled human labor to distinguish the normal red blood cells (RBCs) from sickled cells. There is an unmet need to develop a POC and home-based test to diagnose and monitor SCD and reduce mortality in resource-limited settings. An early-stage and timely diagnosis of SCD can help in the effective management of the disease. In this article, we utilized a smartphone-based image acquisition method for capturing RBC images from the SCD patients in normoxia and hypoxia conditions. A computer algorithm is developed to differentiate RBCs from the patient's blood before and after cell sickling. Using the developed smartphone-based technique, we obtained similar percentage of sickle cells in blood samples as analyzed by conventional method (standard microscope). The developed method of testing demonstrates the potential utility of the smartphone-based test for reducing the overall cost of screening and management for SCD, thus increasing the practicality of smartphone-based screening technique for SCD in low-resource settings. Our setup does not require any special storage requirements and is particularly useful in assessing the severity of the SCD. This is the characteristic advantage of our technique as compared to other hemoglobin-based POC diagnostic techniques.

镰状细胞病 (SCD) 是一种世界范围的血液疾病，会导致疼痛、贫血、器官损伤、中风，甚至死亡。这种情况在撒哈拉以南非洲和其他资源有限的国家更为常见。传统的基于实验室的 SCD 诊断方法既耗时又复杂，并且无法在护理点 (POC) 和家庭环境中进行。基于光学显微镜的分类和计数需要大量的时间、广泛的设置和成本以及熟练的人力来区分正常红细胞 (RBC) 和镰状细胞。开发 POC 和家庭测试来诊断和监测 SCD 并降低资源有限环境中的死亡率的需求尚未得到满足。 SCD 的早期及时诊断有助于有效控制该疾病。在本文中，我们利用基于智能手机的图像采集方法在常氧和缺氧条件下捕获 SCD 患者的红细胞图像。开发了一种计算机算法来区分细胞镰状化前后患者血液中的红细胞。使用开发的基于智能手机的技术，我们获得了与传统方法（标准显微镜）分析相似的血液样本中镰状细胞的百分比。所开发的测试方法证明了基于智能手机的测试在降低 SCD 筛查和管理总体成本方面的潜在效用，从而提高了基于智能手机的 SCD 筛查技术在资源匮乏环境中的实用性。我们的设置不需要任何特殊的存储要求，并且在评估 SCD 的严重性时特别有用。与其他基于血红蛋白的 POC 诊断技术相比，这是我们技术的特征优势。