Smartphone-based sickle cell disease detection and monitoring for point-of-care settings

Abstract

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.

Touchable cell biophysics property recognition platforms enable multifunctional blood smart health care

“…Through cell imaging and analysis (Fig. 6b ) 36 , the morphological and mechanical parameters are shown in Fig. 6 c–f.…”

“…A tunable imaging platform (depth of field: 10 µm) was designed for capturing blood cell images at different focusing surfaces. Images, morphologic (diameter, circularity, axis ratio, and corresponding distribution width), and mechanical (deformability and distribution width) parameters of blood cells were obtained via a developed image algorithm employed on a smartphone 36 – 39 . The integrated data were then used as input for cloud computing, and they were then transformed into vector tables and loaded into image vectors to perform pathological diagnosis and quality monitoring based on a trained neural network 40 , 41 .…”

Development of a Point-of-Care Assay for HIV-1 Viral Load Using Higher Refractive Index Antibody-Coated Microbeads

“…A microfluidic chip was assembled using the PMMA, DSA, and glass slide using previously published methods [ 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ]. The chip design was made in AutoCAD 2015 from Autodesk, Inc. (San Rafael, CA, USA) and uploaded to the UCP software.…”

Development of a Microfluidic Device for CD4+ T Cell Isolation and Automated Enumeration from Whole Blood

“…The AutoCAD software was linked to a laser cutter VLS 2.30 laser cutter (VersaLaser, Scottsdale, AZ, USA). PMMA and DSA sheets were cut according to the design requirements as per previously developed method [ 13 , 14 , 15 , 16 ]. The dimensions of microfluidic channels were selected in such a manner that there were three microchannels in each chip for running two samples and one control.…”