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

A commentary on the development and use of smartphone imaging devices

Current-age smartphones are known for their wide array of functionality and are now being utilized in the field of healthcare and medicine due to their proven capabilities as smartphone imaging devices (SIDs). Recent technical advancements enabled the integration of special add-on lenses with smartphones to transform them into SIDs. With the rising demand for efficient point-of-care (PoC) devices for better diagnostic applications, SIDs will be a one-stop solution. Additionally, portability, user-friendliness and low-cost make it accessible for all even at remote locations. Furthermore, improvements in resolution, magnification and field-of-view (FOV) have attracted the scientific community to use SIDs in various biomedical applications such as disease diagnosis, food quality control and pathogen detection. SIDs can be arranged in various combinational setups by using different illumination sources and optics to achieve suitable contrast and visibility of the specimen under study. This Commentary illustrates the various illumination sources used in SID and also spotlights their design and applications.

Crowdsourced human-based computational approach for tagging peripheral blood smear sample images from Sickle Cell Disease patients using non-expert users

In this paper, we present a human-based computation approach for the analysis of peripheral blood smear (PBS) images images in patients with Sickle Cell Disease (SCD). We used the Mechanical Turk microtask market to crowdsource the labeling of PBS images. We then use the expert-tagged erythrocytesIDB dataset to assess the accuracy and reliability of our proposal. Our results showed that when a robust consensus is achieved among the Mechanical Turk workers, probability of error is very low, based on comparison with expert analysis. This suggests that our proposed approach can be used to annotate datasets of PBS images, which can then be used to train automated methods for the diagnosis of SCD. In future work, we plan to explore the potential integration of our findings with outcomes obtained through automated methodologies. This could lead to the development of more accurate and reliable methods for the diagnosis of SCD.

REASSURED diagnostics at point-of-care in sub-Saharan Africa: A scoping review

Point-of-care (POC) diagnostics that meet the REASSURED criteria are essential in combating the rapid increase and severity of global health emergencies caused by infectious diseases. However, little is known about whether the REASSURED criteria are implemented in regions known to have a high burden of infectious diseases such as sub-Saharan Africa (SSA). This scoping review maps evidence of the use of REASSURED POC diagnostic tests in SSA. The scoping review was guided by the advanced methodological framework of Arksey and O'Malley, and Levac et al. We searched the following electronic databases for relevant literature: Scopus, Dimensions, ProQuest Central, Google Scholar, and EBSCOhost (MEDLINE, CINAHL, as well as AFRICA-WIDE). Two reviewers independently screened abstracts and full-text articles using the inclusion criteria as reference. We appraised the quality of the included studies using the mixed-method appraisal tool (MMAT) version 2018. We retrieved 138 publications, comprising 134 articles and four grey literature articles. Of these, only five articles were included following abstract and full-text screening. The five included studies were all conducted in SSA. The following themes emerged from the eligible articles: quality assurance on accuracy of REASSURED POC diagnostic tests, sustainability of REASSURED POC diagnostic tests, and local infrastructure capability for delivering REASSURED POC diagnostic tests to end users. All five articles had MMAT scores between 90% and 100%. In conclusion, our scoping review revealed limited published research on REASSURED diagnostics at POC in SSA. We recommend primary studies aimed at investigating the implementation of REASSURED POC diagnostic tests in SSA.

Fully integrated point-of-care blood cell count using multi-frame morphology analysis

Point-of-care testing (POCT) of blood cell count (BCC) is an emerging approach that allows laypersons to identify and count whole blood cells through simple manipulation. To date, POCTs for BCC were mainly achieved by "stationary" images through blood smears or single-laity arranged cells in the microwell, making it difficult to obtain statistically sufficient numbers of cells. In this work, we present a fully integrated POCT device solely using "in-flow" imaging of 3 μL fingertip whole blood for improved identification and counting accuracy of BCC analysis. A miniaturized magnetic stirring module was integrated to maintain the temporal stability of cell concentration. A relatively high throughput (∼8000 cells/min) with a 30-fold dilution ratio of whole blood can be tested for as long as 1 h to examine sufficient numbers of cells, and the subclass cell concentration keeps constant. To improve the identification accuracy, multi-frame "in-flow" imaging was used to track the cell motion trails with multi-angle morphology analysis. This proof-of-concept was then validated with healthy whole blood samples and 75 cases of clinical patients with abnormal concentrations of red blood cells (RBCs), white blood cells (WBCs), and platelets (PLT). The average precision (AP) value of WBCs identification was improved from 0.8622 to 0.9934 using the multi-frame analysis method. And the high fitting degrees (>0.98) between our POCT device and the commercial clinical equipment indicated good agreement. This POCT device is user-friendly and cost-effective, making it a potential tool for diagnosing abnormal blood cell morphology or concentration in the field setting.

A novel deep learning approach for sickle cell anemia detection in human RBCs using an improved wrapper-based feature selection technique in microscopic blood smear images

Sickle Cell Anemia (SCA) is a disorder in Red Blood Cells (RBCs) of human blood. Children under five years and pregnant women are mostly affected by SCA. Early diagnosis of this ailment can save lives. In recent years, the computer aided diagnosis of SCA is preferred to resolve this issue. A novel and effective deep learning approach for identification of sickle cell anemia is proposed in this work. Around nine hundred microscopic images of human red blood cells are obtained from the public database 'erythrocytes IDB'. All the images are resized uniformly. About 2048 deep features are extracted from the fully connected layer of pre-trained model InceptionV3. These features are further subjected to classification using optimization-based methods. An improved wrapper-based feature selection technique is implemented using Multi- Objective Binary Grey Wolf Optimization (MO-BGWO) approach with KNN and SVM for classification. The detection of sickle cell is also performed using typical InceptionV3 model by using SoftMax layer. It is observed that the performance of the proposed system seems to be high when compared to the classification using the original InceptionV3 model. The results are validated by various evaluation metrics such as accuracy, precision, sensitivity, specificity and F1-score. The SVM classifier yields high accuracy of about 96%. The optimal subset of deep features along with SVM enhances the system performance in the proposed work. Thus, the proposed approach is appropriate for pathologists to take early clinical decisions on detection of sickle cells.

A ratiometric fluorescence platform based on carbon dots for visual and rapid detection of copper(II) and fluoroquinolones

A simple smartphone-integrated ratiometric fluorescent sensing system for visual determination of copper ions (Cu²⁺) and fluoroquinolones (FQs) was developed based on carbon dots (CDs) which were synthesized through the high-temperature pyrolysis of citric acid. In this system, with the fluorescence resonance energy transfer effect between CDs and 2,3-diaminophenazine (oxOPD), the detection of Cu²⁺ and ciprofloxacin (CIP, an example for FQs) was realized. Cu²⁺ catalyzes the oxidation of OPD to form oxOPD with yellow fluorescence, resulting in the quenching of CDs. In addition, CIP can inhibit the catalytic activity of Cu²⁺ and induce the recovery of CDs fluorescence. Under the excitation of 400 nm, the changes of CDs fluorescence at 472 nm and oxOPD fluorescence at 556 nm were monitored. The detection results showed that the sensing system exhibited good selectivity and sensitivity to Cu²⁺ and CIP with the limit of detection of 2.32 × 10^-8 mol L^-1 and 0.2 ng mL^-1, respectively. In addition, a smartphone was developed as a portable analyzer to capture the change of fluorescence color and quickly analyze the concentration of Cu²⁺ and CIP. The proposed smartphone-based sensing platform has satisfactory sensitivity, and it has application prospects for detecting Cu²⁺ and FQs in food safety monitoring.

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

The development of point-of-care, cost-effective, and easy-to-use assays for the accurate counting of CD4⁺ T cells remains an important focus for HIV-1 disease management. The CD4⁺ T cell count provides an indication regarding the overall success of HIV-1 treatments. The CD4⁺ T count information is equally important for both resource-constrained regions and areas with extensive resources. Hospitals and other allied facilities may be overwhelmed by epidemics or other disasters. An assay for a physician’s office or other home-based setting is becoming increasingly popular. We have developed a technology for the rapid quantification of CD4⁺ T cells. A double antibody selection process, utilizing anti-CD4 and anti-CD3 antibodies, is tested and provides a high specificity. The assay utilizes a microfluidic chip coated with the anti-CD3 antibody, having an improved antibody avidity. As a result of enhanced binding, a higher flow rate can be applied that enables an improved channel washing to reduce non-specific bindings. A wide-field optical imaging system is also developed that provides the rapid quantification of cells. The designed optical setup is portable and low-cost. An ImageJ-based program is developed for the automatic counting of CD4⁺ T cells. We have successfully isolated and counted CD4⁺ T cells with high specificity and efficiency greater than 90%.

A portable impedance microflow cytometer for measuring cellular response to hypoxia

This article presents the development and testing of a low-cost (<$60), portable, electrical impedance-based microflow cytometer for single-cell analysis under a controlled oxygen microenvironment. The system is based on an AD5933 impedance analyzer chip, a microfluidic chip, and an Arduino microcontroller operated by a custom Android application. A representative case study on human red blood cells (RBCs) affected by sickle cell disease is conducted to demonstrate the capability of the cytometry system. Impedance values of sickle blood samples exhibit remarkable deviations from the common reference line obtained from two normal blood samples. Such deviation is quantified by a conformity score, which allows for the measurement of intrapatient and interpatient variations of sickle cell disease. A low conformity score under oxygenated conditions or drastically different conformity scores between oxygenated and deoxygenated conditions can be used to differentiate a sickle blood sample from normal. Furthermore, an equivalent circuit model of a suspended biological cell is used to interpret the electrical impedance of single flowing RBCs. In response to hypoxia treatment, all samples, regardless of disease state, exhibit significant changes in at least one single-cell electrical property, that is, cytoplasmic resistance and membrane capacitance. The overall response to hypoxia is less in normal cells than those affected by sickle cell disease, where the change in membrane capacitance varies from -23% to seven times as compared with -17% in normal cells. The results reported in this article suggest that the developed method of testing demonstrates the potential application for a low-cost screening technique for sickle cell disease and other diseases in the field and low-resource settings. The developed system and methodology can be extended to analyze cellular response to hypoxia in other cell types.

Dengue Detection: Advances in Diagnostic Tools from Conventional Technology to Point of Care

The dengue virus (DENV) is a vector-borne flavivirus that infects around 390 million individuals each year with 2.5 billion being in danger. Having access to testing is paramount in preventing future infections and receiving adequate treatment. Currently, there are numerous conventional methods for DENV testing, such as NS1 based antigen testing, IgM/IgG antibody testing, and Polymerase Chain Reaction (PCR). In addition, novel methods are emerging that can cut both cost and time. Such methods can be effective in rural and low-income areas throughout the world. In this paper, we discuss the structural evolution of the virus followed by a comprehensive review of current dengue detection strategies and methods that are being developed or commercialized. We also discuss the state of art biosensing technologies, evaluated their performance and outline strategies to address challenges posed by the disease. Further, we outline future guidelines for the improved usage of diagnostic tools during recurrence or future outbreaks of DENV.

Techniques for the Detection of Sickle Cell Disease: A Review

Sickle cell disease (SCD) is a widespread disease caused by a mutation in the beta-globin gene that leads to the production of abnormal hemoglobin called hemoglobin S. The inheritance of the mutation could be homozygous or heterozygous combined with another hemoglobin mutation. SCD can be characterized by the presence of dense, sickled cells that causes hemolysis of blood cells, anemia, painful episodes, organ damage, and in some cases death. Early detection of SCD can help to reduce the mortality and manage the disease effectively. Therefore, different techniques have been developed to detect the sickle cell disease and the carrier states with high sensitivity and specificity. These techniques can be screening tests such as complete blood count, peripheral blood smears, and sickling test; confirmatory tests such as hemoglobin separation techniques; and genetic tests, which are more expensive and need to be done in centralized labs by highly skilled personnel. However, advanced portable point of care techniques have been developed to provide a low-cost, simple, and user-friendly device for detecting SCD, for instance coupling solubility tests with portable devices, using smartphone microscopic classifications, image processing techniques, rapid immunoassays, and sensor-based platforms. This review provides an overview of the current and emerging techniques for sickle cell disease detection and highlights the different potential methods that could be applied to help the early diagnosis of SCD.

Fluorescence spectroscopy as a novel technique for premarital screening of sickle cell disorders

Spectral diagnostic screening for sickle cell disease was carried out on volunteer blood samples (N = 100). The samples were subjected to different diagnostic methods including conventional complete blood count (CBC), hemoglobin electrophoresis (HBE) and spectral diagnosis. For the spectral diagnostic method, we discriminated three different characteristic spectral features. In total, 15 samples were sickle cell trait (SCT), 34 samples were sickle cell disease (SCD), and the rest of the samples (N = 51) were normal controls. The spectral discrimination of the three different sets of samples was distinguished on the quantification of fluorescent biomolecules such as tyrosine, tryptophan, NADH, FAD, and porphyrins. The results were compared with the conventional standard CBC and capillary electrophoresis findings. The spectral diagnosis method exhibited a sensitivity and specificity greater than 90 % for the tested samples. This technique requires only 5 mL blood samples, has an analysis time of 20 min, exhibits high accuracy and may be used in small clinics in remote villages.

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

The detection of viruses using imaging techniques is challenging because of the weak scattering of light generated by the targets of sizes in the nanometer range. The system we have developed overcomes the light scattering problems by utilizing antibody-coated microbeads of higher index of refraction that can specifically bind with viruses and increase the acceptance angle. Using the new technology, we have developed a portable, cost-effective, and field-deployable platform for the rapid quantification of HIV-1 viral load for point-of-care (POC) settings. The system combines microfluidics with a wide field of view lensless imaging technology. Highly specific antibodies are functionalized to a glass slide inside a microchip to capture HIV-1 virions. The captured virions are then bound by antibody-conjugated microbeads, which have a higher refraction index. The microbeads-HIV-1 virions complexes generate diffraction patterns that are detected with a custom-built imaging setup and rapidly and accurately quantified by computational analysis. This platform technology enables fast nanoscale virus imaging and quantification from biological samples and thus can play a significant role in the detection and management of viral diseases.

Hand-Held Zoom Micro-Imaging System Based on Microfluidic Chip for Point-of-Care Testing (POCT) of Vaginal Inflammation

BACKGROUND

Vaginitis is a common and very private disease, and the current diagnosis is a frequent go to the hospital for testing.

OBJECTIVE

In order to improve the convenience and speed of detection, in this paper, we have developed a hand-held zoom micro-imaging system based on a microfluidic chip for point-of-care testing (POCT) of vaginal inflammation.

METHODS

This system consists of a microfluidic chip, an optical system and a hand-held zoom system assembled with a mobile phone. In terms of hardware, we designed a self-priming microfluidic chip, which can realize automatic sampling and full mixing of samples. We have also developed an optical system that can be adapted to smartphones, which has a lens group with a 37x magnification function and equipped with a zoom system with a focus range of 4mm to 6mm. In terms of software, we proposed an APP that can accurately identify cocci and can determine the inflammation level.

RESULTS

Compared with the recognition rate of the observers in the hospital, the detection accuracy of the portable recognition system is 95%, and after testing the clinical samples, the results were completely consistent with the hospital diagnosis results. The detection limit was 500 CFU / ml, which the relative error was (0.9 ± 0.3) %, and recognition time is 7 seconds.

CONCLUSION

This system is definitely suitable for women's point-of-care testing (POCT).

Design, Development, and Performance Comparison of Wide Field Lensless and Lens-Based Optical Systems for Point-of-Care Biological Applications

Lensless biological imaging systems are an emerging alternative to conventional microscopic systems because they enable a wide field of view imaging. While most microscopic systems sacrifice the field of view for magnification, lensless systems have taken advantage of small imaging pixel size, projection, digital magnification, and post-processing to compensate for diffracted images. A new lens-based system is designed to have the exact same wide field of view as that of a basic lensless setup. A new compound lens system design is utilized to achieve an explicit aim to have the same fields of view as the lensless setup. Then the characteristics of these two optical imaging setups (lensless and lens-based setups) are compared at this level of complexity to see what the minimal systems principles are needed to achieve the biological imaging goals for simplified and less expensive future designs. For both imaging systems, images of biological entities are recorded with the help of the same CMOS imaging device and computer software. The main contribution of this work is an exhaustive comparison between the performance characteristics of both systems using optical standards and biological images.

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

As a crucial biophysical property, red blood cell (RBC) deformability is pathologically altered in numerous disease states, and biochemical and structural changes occur over time in stored samples of otherwise normal RBCs. However, there is still a gap in applying it further to point-of-care blood devices due to the large external equipment (high-resolution microscope and microfluidic pump), associated operational difficulties, and professional analysis. Herein, we revolutionarily propose a smart optofluidic system to provide a differential diagnosis for blood testing via precise cell biophysics property recognition both mechanically and morphologically. Deformation of the RBC population is caused by pressing the hydrogel via an integrated mechanical transfer device. The biophysical properties of the cell population are obtained by the designed smartphone algorithm. Artificial intelligence-based modeling of cell biophysics properties related to blood diseases and quality was developed for online testing. We currently achieve 100% diagnostic accuracy for five typical clinical blood diseases (90 megaloblastic anemia, 78 myelofibrosis, 84 iron deficiency anemia, 48 thrombotic thrombocytopenic purpura, and 48 thalassemias) via real-world prospective implementation; furthermore, personalized blood quality (for transfusion in cardiac surgery) monitoring is achieved with an accuracy of 96.9%. This work suggests a potential basis for next-generation blood smart health care devices.

Application of smartphone-based spectroscopy to biosample analysis: A review

The emergence of the smartphones has brought extensive changes to our lifestyles, from communicating with one another, to shopping and enjoyment of entertainment, and from studying to functioning at the workplace (and in the field). At the same time, this portable device has also provided new possibilities in scientific research and applications. Based on the growing awareness of good health management, researchers have coupled health monitoring to smartphone sensing technologies. Along the way, there have been developed a variety of smartphone-based optical detection platforms for analyzing biological samples, including standalone smartphone units and integrated smartphone sensing systems. In this review, we outline the applications of smartphone-based optical sensors for biosamples. These applications focus mainly on three aspects: Microscopic imaging sensing, colorimetric sensing and luminescence sensing. We also discuss briefly some limitations of the current state of smartphone-based spectroscopy and present prospects of the future applicability of smartphone sensors.