Emerging point-of-care technologies for anemia detection

Sample preparation and detection methods in point-of-care devices towards future at-home testing

Timely and accurate diagnosis is critical for effective healthcare, yet nearly half the global population lacks access to basic diagnostics. Point-of-care (POC) testing offers partial solutions by enabling low-cost, rapid diagnosis at the patient's location. At-home POC devices have the potential to advance preventive care and early disease detection. Nevertheless, effective sample preparation and detection methods are essential for accurate results. This review surveys recent advances in sample preparation and detection methods at POC. The goal is to provide an in-depth understanding of how these technologies can enhance at-home POC devices. Lateral flow assays, nucleic acid tests, and virus detection methods are at the forefront of POC diagnostic technology, offering rapid and sensitive tools for identifying and measuring pathogens, biomarkers, and viral infections. By illuminating cutting-edge research on assay development for POC diagnostics, this review aims to accelerate progress towards widely available, user-friendly, at-home health monitoring tools that empower individuals in personalized healthcare in the future.

Revolutionizing anemia detection: integrative machine learning models and advanced attention mechanisms

This study addresses the critical issue of anemia detection using machine learning (ML) techniques. Although a widespread blood disorder with significant health implications, anemia often remains undetected. This necessitates timely and efficient diagnostic methods, as traditional approaches that rely on manual assessment are time-consuming and subjective. The present study explored the application of ML - particularly classification models, such as logistic regression, decision trees, random forest, support vector machines, Naïve Bayes, and k-nearest neighbors - in conjunction with innovative models incorporating attention modules and spatial attention to detect anemia. The proposed models demonstrated promising results, achieving high accuracy, precision, recall, and F1 scores for both textual and image datasets. In addition, an integrated approach that combines textual and image data was found to outperform the individual modalities. Specifically, the proposed AlexNet Multiple Spatial Attention model achieved an exceptional accuracy of 99.58%, emphasizing its potential to revolutionize automated anemia detection. The results of ablation studies confirm the significance of key components - including the blue-green-red, multiple, and spatial attentions - in enhancing model performance. Overall, this study presents a comprehensive and innovative framework for noninvasive anemia detection, contributing valuable insights to the field.

Microstrip isoelectric focusing with deep learning for simultaneous screening of diabetes, anemia, and thalassemia

BACKGROUND

Hemoglobin (Hb) is an important protein in red blood cells and a crucial diagnostic indicator of diseases, e.g., diabetes, thalassemia, and anemia. However, there is a rare report on methods for the simultaneous screening of diabetes, anemia, and thalassemia. Isoelectric focusing (IEF) is a common separative tool for the separation and analysis of Hb. However, the current analysis of IEF images is time-consuming and cannot be used for simultaneous screening. Therefore, an artificial intelligence (AI) of IEF image recognition is desirable for accurate, sensitive, and low-cost screening.

RESULTS

Herein, we proposed a novel comprehensive method based on microstrip isoelectric focusing (mIEF) for detecting the relative content of Hb species. There was a good coincidence between the quantitation of Hb via a conventional automated hematology analyzer and the one via mIEF with R2 = 0.9898. Nevertheless, our results showed that the accuracy of disease diagnosis based on the quantification of Hb species alone is as low as 69.33 %, especially for the simultaneous screening of multiple diseases of diabetes, anemia, alpha-thalassemia, and beta-thalassemia. Therefore, we introduced a ResNet1D-based diagnosis model for the improvement of screening accuracy of multiple diseases. The results showed that the proposed model could achieve a high accuracy of more than 90 % and a good sensitivity of more than 96 % for each disease, indicating the overwhelming advantage of the mIEF method combined with deep learning in contrast to the pure mIEF method.

SIGNIFICANCE

Overall, the presented method of mIEF with deep learning enabled, for the first time, the absolute quantitative detection of Hb, relative quantitation of Hb species, and simultaneous screening of diabetes, anemia, alpha-thalassemia, and beta-thalassemia. The AI-based diagnosis assistant system combined with mIEF, we believe, will help doctors and specialists perform fast and precise disease screening in the future.

Understanding Rare Anemias: Emerging Frontiers for Diagnosis and Treatment

Background-This review provides a comprehensive overview of rare anemias, emphasizing their hereditary and acquired causes, diagnostic advancements, and evolving treatment strategies. It outlines the significance of rare anemias within public health, historical challenges in recognition and treatment, and the role of European initiatives like ENERCA and EuroBloodNet in advancing care. Content-This document discusses diagnostic technologies like next-generation sequencing and the impact of artificial intelligence, alongside the promising avenues of gene therapy, targeted drug treatments, and stem cell transplantation. It underscores the importance of a patient-tailored approach, advances in diagnostic tools, and the necessity for continued research, patient advocacy, and international collaboration to improve outcomes for individuals with rare anemias.

Detecting labile heme and ferroptosis through 'turn-on' fluorescence and lipid droplet localization post Fe2+ sensing

Iron, a crucial biologically active ion essential for metabolic processes in living organisms, plays a vital role in biological functions, and imbalances in iron levels can lead to various diseases. In this study, we have developed two simple "turn-on" fluorescent probes, NOPy and NOCN, for the quick and selective detection of Fe2+ at nanomolar levels (LOD of 35 nM), accompanied by significant absorption and emission shifts, along with colorimetric demarcation. Both fluorophores exhibit an excellent "turn-on" emission response upon encountering Fe2+ in the cells. Flow cytometry and confocal fluorescence imaging studies demonstrate enhanced fluorescence signals in response to labile iron, efficiently detecting heme during erastin-induced ferroptosis. Interestingly, we also observed that the product formed after Fe2+ sensing localizes within the lipid droplets. These water-soluble and highly sensitive reactive probes, NOPy and NOCN, enable investigations of iron-dependent physiological and pathological conditions. The development of these probes represents an advancement in the field, offering a rapid and selective means for detecting Fe2+ with minimal cytotoxicity.

Noninvasive hemoglobin quantification across different cohorts using a wearable diffuse reflectance spectroscopy system

Quantifying hemoglobin is vital yet invasive through blood draws. We developed a wearable diffuse reflectance spectroscopy device comprising control and sensor boards with photodiodes and light-emitting diodes to noninvasively determine hemoglobin. Neural networks enabled recovery of optical parameters for chromophore fitting to calculate hemoglobin. Testing healthy and elderly subjects revealed strong correlation (r=0.9) between our system and invasive methods after data conversion. Bland-Altman analysis demonstrated tight 95% limits of agreement from -1.98 to 1.98 g/dL between the DRS and invasive hemoglobin concentrations. By spectroscopically isolating hemoglobin absorption, interference from melanin was overcome. Our device has the potential for future integration into wearable technology, enabling hemoglobin level tracking.

Point-of-Care Diagnostic Test for Beta-Thalassemia

Hemoglobin (Hb) disorders are among the most common monogenic diseases affecting nearly 7% of the world population. Among various Hb disorders, approximately 1.5% of the world population carries β-thalassemia (β-Thal), affecting 40,000 newborns every year. Early screening and a timely diagnosis are essential for β-thalassemia patients for the prevention and management of later clinical complications. However, in Africa, Southern Europe, the Middle East, and Southeast Asia, where β-thalassemia is most prevalent, the diagnosis and screening for β-thalassemia are still challenging due to the cost and logistical burden of laboratory diagnostic tests. Here, we present Gazelle, which is a paper-based microchip electrophoresis platform that enables the first point-of-care diagnostic test for β-thalassemia. We evaluated the accuracy of Gazelle for the β-Thal screening across 372 subjects in the age range of 4-63 years at Apple Diagnostics lab in Mumbai, India. Additionally, 30 blood samples were prepared to mimic β-Thal intermediate and β-Thal major samples. Gazelle-detected levels of Hb A, Hb F, and Hb A2 demonstrated high levels of correlation with the results reported through laboratory gold standard high-performance liquid chromatography (HPLC), yielding a Pearson correlation coefficient = 0.99. This ability to obtain rapid and accurate results suggests that Gazelle may be suitable for the large-scale screening and diagnosis of β-Thal.

Use of point-of-care haemoglobin tests to diagnose childhood anaemia in low- and middle-income countries: A systematic review

OBJECTIVES

Anaemia is a major cause of mortality and transfusion in children in low- and middle-income countries (LMICs); however, current diagnostics are slow, costly and frequently unavailable. Point-of-care haemoglobin tests (POC(Hb)Ts) could improve patient outcomes and use of resources by providing rapid and affordable results. We systematically reviewed the literature to investigate what, where and how POC(Hb)Ts are being used by health facilities in LMICs to diagnose childhood anaemia, and to explore challenges to their use.

METHODS

We searched a total of nine databases and trial registries up to 10 June 2022 using the concepts: anaemia, POC(Hb)T, LMIC and clinical setting. Adults ≥21 years and literature published >15 years ago were excluded. A single reviewer conducted screening, data extraction and quality assessment (of diagnostic studies) using QUADAS-2. Outcomes including POC(Hb)T used, location, setting, challenges and diagnostic accuracy were synthesised.

RESULTS

Of 626 records screened, 41 studies were included. Evidence is available on the use of 15 POC(Hb)Ts in hospitals (n = 28, 68%), health centres (n = 9, 22%) and clinics/units (n = 10, 24%) across 16 LMICs. HemoCue (HemoCue AB, Ängelholm, Sweden) was the most used test (n = 31, 76%). Key challenges reported were overestimation of haemoglobin concentration, clinically unacceptable limits of agreement, errors/difficulty in sampling, environmental factors, cost, inter-observer variability and supply of consumables. Five POC(Hb)Ts (33%) could not detect haemoglobin levels below 4.5 g/dL. Diagnostic accuracy varied, with sensitivity and specificity to detect anaemia ranging from 24.2% to 92.2% and 70% to 96.7%, respectively.

CONCLUSIONS

POC(Hb)Ts have been successfully utilised in health facilities in LMICs to diagnose childhood anaemia. However, limited evidence is available, and challenges exist that must be addressed before wider implementation. Further research is required to confirm accuracy, clinical benefits and cost-effectiveness.

Assessing the Predictive Power of the Hemoglobin/Red Cell Distribution Width Ratio in Cancer: A Systematic Review and Future Directions

Background and Objectives: The hemoglobin (Hb)/red cell distribution width (RDW) ratio has emerged as an accessible, repeatable, and inexpensive prognostic factor that may predict survival in cancer patients. The focus of this systematic review is to investigate the prognostic role of the Hb/RDW ratio in cancer and the implications for clinical practice. Materials and Methods: A literature search of PubMed, Scopus, and Web of Science databases was performed by an independent author between 18 March and 30 March 2023 to collect relevant literature that assessed the prognostic value of the Hb/RDW ratio in cancer. Overall survival (OS), progression-free survival (PFS), and the association of these with the Hb/RDW ratio were considered to be the main endpoints. Results: Thirteen retrospective studies, including 3818 cancer patients, were identified and involved in this review. It was observed that, when patients with a high vs. low Hb/RDW ratio were compared, those with a lower Hb/RDW ratio had significantly poorer outcomes (p < 0.05). In lung cancer patients, a one-unit increase in the Hb/RDW ratio reduces mortality by 1.6 times, whilst in esophageal squamous-cell carcinoma patients, a lower Hb/RDW ratio results in a 1.416-times greater risk of mortality. Conclusions: A low Hb/RDW ratio was associated with poor OS and disease progression in patients with cancer. This blood parameter should be considered a standard biomarker in clinical practice for predicting OS and PFS in cancer patients. Future searches will be necessary to determine and standardize the Hb/RDW cut-off value and to assess whether the Hb/RDW ratio is optimal as an independent prognostic factor or if it requires incorporation into risk assessment models for predicting outcomes in cancer patients.

Non-invasive detection of anemia using lip mucosa images transfer learning convolutional neural networks

Anemia is defined as a drop in the number of erythrocytes or hemoglobin concentration below normal levels in healthy people. The increase in paleness of the skin might vary based on the color of the skin, although there is currently no quantifiable measurement. The pallor of the skin is best visible in locations where the cuticle is thin, such as the interior of the mouth, lips, or conjunctiva. This work focuses on anemia-related pallors and their relationship to blood count values and artificial intelligence. In this study, a deep learning approach using transfer learning and Convolutional Neural Networks (CNN) was implemented in which VGG16, Xception, MobileNet, and ResNet50 architectures, were pre-trained to predict anemia using lip mucous images. A total of 138 volunteers (100 women and 38 men) participated in the work to develop the dataset that contains two image classes: healthy and anemic. Image processing was first performed on a single frame with only the mouth area visible, data argumentation was preformed, and then CNN models were applied to classify the dataset lip images. Statistical metrics were employed to discriminate the performance of the models in terms of Accuracy, Precision, Recal, and F1 Score. Among the CNN algorithms used, Xception was found to categorize the lip images with 99.28% accuracy, providing the best results. The other CNN architectures had accuracies of 96.38% for MobileNet, 95.65% for ResNet %, and 92.39% for VGG16. Our findings show that anemia may be diagnosed using deep learning approaches from a single lip image. This data set will be enhanced in the future to allow for real-time classification.

Anemia detection through non-invasive analysis of lip mucosa images

This paper aims to detect anemia using images of the lip mucosa, where the skin tissue is thin, and to confirm the feasibility of detecting anemia noninvasively and in the home environment using machine learning (ML). Data were collected from 138 patients, including 100 women and 38 men. Six ML algorithms: artificial neural network (ANN), decision tree (DT), k-nearest neighbors (KNN), logistic regression (LR), naive bayes (NB), and support vector machine (SVM) which are widely used in medical applications, were used to classify the collected data. Two different data types were obtained from participants' images (RGB red color values and HSV saturation values) as features, with age, sex, and hemoglobin levels utilized to perform classification. The ML algorithm was used to analyze and classify images of the lip mucosa quickly and accurately, potentially increasing the efficiency of anemia screening programs. The accuracy, precision, recall, and F-measure were evaluated to assess how well ML models performed in predicting anemia. The results showed that NB reported the highest accuracy (96%) among the other ML models used. DT, KNN and ANN reported an accuracies of (93%), while LR and SVM had an accuracy of (79%) and (75%) receptively. This research suggests that employing ML approaches to identify anemia will help classify the diagnosis, which will then help to create efficient preventive measures. Compared to blood tests, this noninvasive procedure is more practical and accessible to patients. Furthermore, ML algorithms may be created and trained to assess lip mucosa photos at a minimal cost, making it an affordable screening method in regions with a shortage of healthcare resources.

A Plasmonic Fluor-Lightened Microneedle Array Enables Ultrasensitive Multitarget Whole Blood Diagnosis of Anemia in A Paper Origami-Based Device

This work reports a portable, origami-type paper device with a plasmonic fluor-labeled microneedle sensing module for the multiplexed quantification of anemia biomarkers in whole blood. Sequential steps, including serum separation, target enrichment, and multiplexed readout by a gel imager, are rapidly accomplished with the flexible and highly integrated device. The microneedle array enabled efficient sampling of trace targets from ng mL^-1 to pg mL^-1 level. Combined with the plasmonic fluor label, the signal is improved by ≈7.6 folds compared with the flat substrate-based assay. The device is applied to simultaneously quantify hemoglobin (Hb), ferritin, folic acid (FA), and vitamin B₁₂ (VB₁₂ ), which are four anemia biomarkers distributed in different environments with different concentration ranges. Featured by the small sample volume (150 µL), short assay time (20 min), low cost (2 $), robust stability, and user-friendliness, the device is promising for the rapid and accurate diagnosis of anemia in real practice.

Last Trends in Point-of-Care (POC) Diagnostics for the Management of Hematological Indices in Home Care Patients

Today, complete blood count (CBC) analyses are highly automated and allow for high throughput and accurate and reliable results. However, new analytical tools are in great demand to provide simple, rapid and cost-effective management of hematological indices in home care patients. Chronic disease monitoring at home has become a benefit for patients who are finding cost savings in programs designed to monitor/treat patients in offsite locations. This review reports the latest trends in point-of-care (POC) diagnostics useful for home testing of key hematological counts that may be affected during home therapy treatment.

Simultaneous quantitative detection of hematocrit and hemoglobin from whole blood using a multiplexed paper sensor with a smartphone interface

We report a highly accurate single-step label-free testing technology for simultaneous and independent hematocrit (Hct) and hemoglobin (Hb) level detection from a drop of whole blood by employing a disposable paper strip sensor interfaced with a portable impedimetric device. The paper strip is fabricated by in situ automated printing of a customized electrode template on the non-glossy side of a commercially available photo paper substrate followed by graphite deposition. The integrated platform device technology additionally includes a compact detection cum readout unit comprising a high precision impedance converter system that combines an on-board frequency generator with an analog-to-digital converter evaluation board, collectively interfaced with a central processor, calibration circuit, and smartphone. Employing a dispensed blood sample volume of 25 μL, the device is shown to have a sensitivity of 92 Ω/Hct and 287 Ω/Hb at an optimal frequency of 57 kHz. The respective linear response regimes appear to be wide enough to cover physiologically relevant limits, with excellent stability and reproducibility. Validation with clinical samples reveals limits of detection of Hct and Hb levels as low as 4.66% and 1.89 g dL^-1, respectively, which are beyond the quantitative capability of commonly used affordable point of care test kits. The envisaged paradigm of rapid, robust, highly accurate, energy-efficient, simple, user-friendly, multiplex portable detection, obviating any possible ambiguities in interpretation due to common artefacts of colorimetric detection technologies such as optical interference with the image analytical procedure due to the inherent redness of blood samples and background illumination, renders this ideal for deployment in resource-limited settings.

OcclusionChip: A functional microcapillary occlusion assay complementary to ektacytometry for detection of small-fraction red blood cells with abnormal deformability

Red blood cell (RBC) deformability is a valuable hemorheological biomarker that can be used to assess the clinical status and response to therapy of individuals with sickle cell disease (SCD). RBC deformability has been measured by ektacytometry for decades, which uses shear or osmolar stress. However, ektacytometry is a population based measurement that does not detect small-fractions of abnormal RBCs. A single cell-based, functional RBC deformability assay would complement ektacytometry and provide additional information. Here, we tested the relative merits of the OcclusionChip, which measures RBC deformability by microcapillary occlusion, and ektacytometry. We tested samples containing glutaraldehyde-stiffened RBCs for up to 1% volume fraction; ektacytometry detected no significant change in Elongation Index (EI), while the OcclusionChip showed significant differences in Occlusion Index (OI). OcclusionChip detected a significant increase in OI in RBCs from an individual with sickle cell trait (SCT) and from a subject with SCD who received allogeneic hematopoietic stem cell transplant (HSCT), as the sample was taken from normoxic (pO2:159 mmHg) to physiologic hypoxic (pO2:45 mmHg) conditions. Oxygen gradient ektacytometry detected no difference in EI for SCT or HSCT. These results suggest that the single cell-based OcclusionChip enables detection of sickle hemoglobin (HbS)-related RBC abnormalities in SCT and SCD, particularly when the HbS level is low. We conclude that the OcclusionChip is complementary to the population based ektacytometry assays, and providing additional sensitivity and capacity to detect modest abnormalities in red cell function or small populations of abnormal red cells.

A retrospective case study of successful translational research: Gazelle Hb variant point-of-care diagnostic device for sickle cell disease

Evaluation researchers at Clinical and Translational Science Award (CTSA) hubs are conducting retrospective case studies to evaluate the translational research process. The objective of this study was to deepen knowledge of the translational process and identify contributors to successful translation. We investigated the successful translation of the HemeChip, a low-cost point-of-care diagnostic device for sickle cell disease, using a protocol for retrospective translational science case studies of health interventions developed by evaluators at the National Health Institutes (NIH) and CTSA hubs. Development of the HemeChip began in 2013 and evidence of device use and impact on public health is growing. Data collection methods included five interviews and a review of press, publications, patents, and grants. Barriers to translation included proving novelty, manufacturing costs, fundraising, and academic-industry relations. Facilitators to translation were CTSA pilot program funding, university resources, entrepreneurship training, due diligence, and collaborations. The barriers to translation, how they were overcome, and the key facilitators identified in this case study pinpoint areas for consideration in future funding mechanisms and the infrastructure required to enable successful translation.

Point-of-care microchip electrophoresis for integrated anemia and hemoglobin variant testing

Anemia affects over 25% of the world's population with the heaviest burden borne by women and children. Genetic hemoglobin (Hb) variants, such as sickle cell disease, are among the major causes of anemia. Anemia and Hb variant are pathologically interrelated and have an overlapping geographical distribution. We present the first point-of-care (POC) platform to perform both anemia detection and Hb variant identification, using a single paper-based electrophoresis test. Feasibility of this new integrated diagnostic approach is demonstrated via testing individuals with anemia and/or sickle cell disease. Hemoglobin level determination is performed by an artificial neural network (ANN) based machine learning algorithm, which achieves a mean absolute error of 0.55 g dL^-1 and a bias of -0.10 g dL^-1 against the gold standard (95% limits of agreement: 1.5 g dL^-1) from Bland-Altman analysis on the test set. Resultant anemia detection is achieved with 100% sensitivity and 92.3% specificity. With the same tests, subjects with sickle cell disease were identified with 100% sensitivity and specificity. Overall, the presented platform enabled, for the first time, integrated anemia detection and hemoglobin variant identification using a single point-of-care test.