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Detection of iron deficiency anemia by medical images: a comparative study of machine learning algorithms. BioData mining BACKGROUND:Anemia is one of the global public health problems that affect children and pregnant women. Anemia occurs when the level of red blood cells within the body decreases or when the structure of the red blood cells is destroyed or when the Hb level in the red blood cell is below the normal threshold, which results from one or more increased red cell destructions, blood loss, defective cell production or a depleted sum of Red Blood Cells. METHODS:The method used in this study is divided into three phases: the datasets were gathered, which is the palm, pre-processed the image, which comprised; Extracted images, and augmented images, segmented the Region of Interest of the images and acquired their various components of the CIE L*a*b* colour space (also referred to as the CIELAB), and finally developed the proposed models for the detection of anemia using the various algorithms, which include CNN, k-NN, Nave Bayes, SVM, and Decision Tree. The experiment utilized 527 initial datasets, rotation, flipping and translation were utilized and augmented the dataset to 2635. We randomly divided the augmented dataset into 70%, 10%, and 20% and trained, validated and tested the models respectively. RESULTS:The results of the study justify that the models performed appropriately when the palm is used to detect anemia, with the Naïve Bayes achieving a 99.96% accuracy while the SVM achieved the lowest accuracy of 96.34%, as the CNN also performed better with an accuracy of 99.92% in detecting anemia. CONCLUSIONS:The invasive method of detecting anemia is expensive and time-consuming; however, anemia can be detected through the use of non-invasive methods such as machine learning algorithms which is efficient, cost-effective and takes less time. In this work, we compared machine learning models such as CNN, k-NN, Decision Tree, Naïve Bayes, and SVM to detect anemia using images of the palm. Finally, the study supports other similar studies on the potency of the Machine Learning Algorithm as a non-invasive method in detecting iron deficiency anemia. 10.1186/s13040-023-00319-z
Comparing Non-Invasive Spectrophotometry to Hematology Analysis for Hemoglobin Measurements in Sickle Cell Disease Patients. Journal of clinical medicine Patients with sickle cell disease (SCD) require repeated blood sampling for hemoglobin (Hb) concentration measurements. The primary aim of this study was to compare non-invasive spectrophotometric hemoglobin (SpHb, g/dL) measurements to those taken via an automated hematology analyzer (Hb, g/dL) in patients with SCD visiting outpatient clinics and to investigate the correlations and agreements between both measurement techniques. Secondarily, we aimed to identify the SpHb cut-off concentration for the diagnosis of anemia and to monitor the effects of the pleth variability index (PVI, %) and perfusion index (PI) on SpHb measurements. The results gained from the examination of one hundred and fifty-eight patients indicated that the SpHb measurements overestimated the lab Hb concentrations, with a mean (SpHb-Hb) bias of 0.82 g/dL (SD 1.29). The SpHb measurements were positively correlated with the Hb measurements (Kendall's tau correlation (τ), = 158, τ = 0.68, < 0.001), with an intra-class correlation (ICC) of 0.67 and a 95% CI from 0.57 to 0.74 ( = 0.000). The SpHb cut-off concentration to diagnose anemia was 11.4 and 11.7 g/dL for males and females, respectively. SpHb sensitivity was low for males and females at 64.4% and 57.1%; however, the specificity was higher at 90.9% and 75%, with positive predictive values (PPVs) of 95.6 and 85.7, respectively. No correlation existed between SpHb measurements and the PVI (%) in contrast with a moderate correlation with the PI (r = 0.049, = 0.54, and r = 0.36, < 0.001, respectively). The mean PI was low at 2.52 ± 1.7. In conclusion, the SpHb measurements were consistently higher than the lab Hb concentrations, with a positive correlation. The sensitivity and precision of the SpHb measurements were lower than expected. However, the SpHb specificity and its positive predictive values (PPVs) indicated that it is less likely for a patient with a positive SpHb test result for anemia to be non-anemic. These results will allow SpHb measurement to play a role in excluding the presence of anemia. In light of the low PI values determined, the SpHb measurements were challenging to take and, thus, require further technological improvements. 10.3390/jcm12247517
Deep-Learning-Based Hemoglobin Concentration Prediction and Anemia Screening Using Ultra-Wide Field Fundus Images. Frontiers in cell and developmental biology Anemia is the most common hematological disorder. The purpose of this study was to establish and validate a deep-learning model to predict Hgb concentrations and screen anemia using ultra-wide-field (UWF) fundus images. The study was conducted at Peking Union Medical College Hospital. Optos color images taken between January 2017 and June 2021 were screened for building the dataset. ASModel_UWF using UWF images was developed. Mean absolute error (MAE) and area under the receiver operating characteristics curve (AUC) were used to evaluate its performance. Saliency maps were generated to make the visual explanation of the model. ASModel_UWF acquired the MAE of the prediction task of 0.83 g/dl (95%CI: 0.81-0.85 g/dl) and the AUC of the screening task of 0.93 (95%CI: 0.92-0.95). Compared with other screening approaches, it achieved the best performance of AUC and sensitivity when the test dataset size was larger than 1000. The model tended to focus on the area around the optic disc, retinal vessels, and some regions located at the peripheral area of the retina, which were undetected by non-UWF imaging. The deep-learning model ASModel_UWF could both predict Hgb concentration and screen anemia in a non-invasive and accurate way with high efficiency. 10.3389/fcell.2022.888268
NIR-based Sensing System for Non-invasive Detection of Hemoglobin for Point-of-care Applications. Current medical imaging BACKGROUND:Hemoglobin is an essential biomolecule for the transportation of oxygen, therefore, its assessment is also important to be done frequently in numerous clinical practices. Traditional invasive techniques have concomitant shortcomings, such as time delay, the onset of infections, and discomfort, which necessitate a non-invasive hemoglobin estimation solution to get rid of these constraints in health informatics. Currently, various techniques are underway in the allied domain, and scanty products are also feasible in the market. However, due to the low satisfaction rate, invasive solutions are still assumed as the gold standard. Recently introduced technologies effectively evolved as optical spectroscopy and digital photographic concepts on different sensing spots, e.g., fingertip, palpebral conjunctiva, bulbar conjunctiva, and fingernail. Productive sensors develop more than eight wavelengths to compute hemoglobin concentration and four wavelengths to display only Hb-index (trending of hemoglobin) either in disposable adhesive or reusable cliptype sensor's configuration. OBJECTIVE:This study aims at an optimistic optical spectroscopic technique to measure hemoglobin concentration and conditional usability of non-invasive blood parameters' diagnostics at point-ofcare. METHODS:Two distinguishable light emitting sources (810 nm and 1300 nm) are utilized at isosbestic points with a single photodetector (800-1700 nm). With this purpose, reusable finger probe assembly is facilitated in transmittance mode based on the newly offered sliding mechanism to block ambient light. RESULTS:Investigation with proposed design presents correlation coefficients between reference hemoglobin and every individual feature, a multivariate linear regression model for highly correlated independent features. Moreover, principal component analytical model with multivariate linear regression offers mean bias of 0.036 and -0.316 g/dL, precision of 0.878 and 0.838 and limits of agreement from -1.685 to 1.758 g/dL and -1.790 to 1.474 g/dL for 18 and 21 principal components, respectively. CONCLUSION:The encouraging readouts emphasize favorable precision; therefore, it is proposed that the sensing system is amenable to assess hemoglobin in settings with limited resources and strengthening future routes for the point of care applications. 10.2174/1573405617666210823100316
Non-invasive hemoglobin estimation from conjunctival images using deep learning. Medical engineering & physics Hemoglobin, a crucial protein found in erythrocytes, transports oxygen throughout the body. Deviations from optimal hemoglobin levels in the blood are linked to medical conditions, serving as diagnostic markers for certain diseases. The hemoglobin level is usually measured invasively with different devices using the blood sample. In the physical interpretation, some signs are traditionally used. These signs are the palms, face, nail beds, pallor of the conjunctiva, and palmar wrinkles. Studies have shown that conjunctival pallor can yield more effective results in detecting anemia than the pallor of the palms or nail beds. This study is aimed to predict the hemoglobin level by deep learning method, non-invasive, cheap, fast, high accuracy, and without creating medical waste. In this context, conjunctival images and age, weight, height, gender, and hemoglobin values were collected from 388 people who donated blood to the Turkish Red Crescent. A dataset was generated by augmenting the gathered data with body mass index data. Within the scope of this investigation, the limits of agreement (LoA) value at a 95% confidence interval was computed to be 1.23 g/dL, while the bias was established as 0.26 g/dL. The mean absolute percentage error (MAPE) values were determined to be 3.4%, and the root mean squared error (RMSE) was calculated to be 0.68 g/dL. These findings exhibit a successful outcome compared to similar investigations, signifying that this non-invasive method can be employed for hemoglobin level estimation. Furthermore, the estimated hemoglobin levels could aid in diagnosing several hemoglobin-related ailments. 10.1016/j.medengphy.2023.104038
An intelligent non-invasive system for automated diagnosis of anemia exploiting a novel dataset. Artificial intelligence in medicine Anemia is a condition in which the oxygen-carrying capacity of red blood cells is insufficient to meet the body's physiological needs. It affects billions of people worldwide. An early diagnosis of this disease could prevent the advancement of other disorders. Traditional methods used to detect anemia consist of venipuncture, which requires a patient to frequently undergo laboratory tests. Therefore, anemia diagnosis using noninvasive and cost-effective methods is an open challenge. The pallor of the fingertips, palms, nail beds, and eye conjunctiva can be observed to establish whether a patient suffers from anemia. This article addresses the above challenges by presenting a novel intelligent system, based on machine learning, that supports the automated diagnosis of anemia. This system is innovative from different points of view. Specifically, it has been trained on a dataset that contains eye conjunctiva photos of Indian and Italian patients. This dataset, which was created using a very strict experimental set, is now made available to the Scientific Community. Moreover, compared to previous systems in the literature, the proposed system uses a low-cost device, which makes it suitable for widespread use. The performance of the learning algorithms utilizing two different areas of the mucous membrane of the eye is discussed. In particular, the RUSBoost algorithm, when appropriately trained on palpebral conjunctiva images, shows good performance in classifying anemic and nonanemic patients. The results are very robust, even when considering different ethnicities. 10.1016/j.artmed.2022.102477
A non-invasive approach to monitor anemia during long-duration spaceflight with retinal fundus images and deep learning. Life sciences in space research During spaceflight, astronauts can experience significantly higher levels of hemolysis. With future space missions exposing astronauts to longer periods of microgravity, such as missions to Mars, there will be a need to better understand this phenomenon. We have proposed that retinal fundus photography and deep learning may be utilized to help further understand this microgravity-induced, anemic process for future spaceflight. By utilizing astronaut and terrestrial analog metadata, a foundation can be built to develop an algorithm that allows for non-invasive retinal imaging to quantify hemoglobin levels and detect anemia during spaceflight. This approach would allow for a non-invasive retinal photograph that can be done frequently during spaceflight as opposed to an invasive blood draw and subsequent tests. 10.1016/j.lssr.2022.04.004
Developing a novel device based on a new technology for non-invasive measurement of blood biomarkers irrespective of skin color. German medical science : GMS e-journal Background:Human hemoglobin is a tetrameric metalloporphyrin. The heme part contains iron radicle and porphyrin. The globin part consists of two pairs of amino-acid chains. The absorption spectrum of hemoglobin spans from 250 nm to as high as 2,500 nm, with high coefficients reported in blue and green color zone. The visible absorption spectrum of deoxyhemoglobin has one, while the visible absorption spectrum of oxyhemoglobin shows two peaks. Objective:(1) To study absorption spectrometry of hemoglobin in 420 to 600 nm range; (2) to conduct preclinical experiments to validate a new device and technology based on green color absorption by hemoglobin; (3) to use this new technology and device for phase 1 study in healthy human volunteers for confirmation. Design material and methods:(1) Checking absorption spectrometry of hemoglobin in venous blood. We measured absorption spectrometry of 25 mother-baby pairs as an observational study. Readings were plotted from 400 nm to 560 nm. These included peaks, flat lines and deeps. Graph tracings of all samples - cord blood and maternal blood - showed similar patterns. (2) Preclinical experiments were set up (a) to correlate the reflection of green light by hemoglobin and concentration of hemoglobin, (b) to correlate concentration of O and reflection of green light related to oxyhemoglobin, (c) to correlate concentration of melanin in upper and the hemoglobin in lower layer of tissue phantom and to check the sensitivity of new device with green light for measuring Hb in presence of high levels of melanin, and lastly (d) to check if the new device can measure changes in oxy-hemoglobin and deoxy-hemoglobin, again in presence of high levels of melanin with normal as well as with low levels of hemoglobin. The experiments using bilayer tissue phantom were conducted with horse blood in lower cup as dermal tissue phantom and synthetic melanin in upper layer as epidermal tissue phantom. (3) Phase 1 observational studies following a protocol approved by the institutional review board (IRB) were done in two cohorts. Readings were taken using our device and a commercially available pulse oximeter. In the comparison arm we had Point of Care (POC) Hb test (HemoCu or iSTAT blood test). We had 127 data points of POC Hb test and 170 data points for our device and pulse oximeters. This device uses two wavelengths from the visible spectrum of light and uses reflected light. Light of specific wavelengths is shone on the skin of the individual, and the reflected light is collected as 'optical signal'. This optical signal - after conversion to electrical signal - is processed and finally analysed with a digital display on the screen. Melanin is accounted using Von Luschan's chromatic scale (VLS) and a specially designed algorithm. Results:In this set of various preclinical experiments using different concentrations of hemoglobin and melanin, we indeed demonstrated good sensitivity of our device. It could pick up signals from hemoglobin despite high levels of melanin. Our device is a non-invasive device to measure hemoglobin like a pulse oximeter. Results of our device and pulse oximeter were compared with those by POC Hb test like HemoCu and iSTAT. Our device showed better trending linearity and concordance than a pulse oximeter. Since the absorption spectrum of hemoglobin is the same is new-borns and adults, we could develop one device for all age groups and for people of all colors. Furthermore, the light is shone on the wrist of the individual and is then measured. So, in future this device has the potential of being incorporated in a wearable or smart watch technology. 10.3205/000323
Noninvasive Portable Hemoglobin Concentration Monitoring System Using Optical Sensor for Anemia Disease. Healthcare (Basel, Switzerland) Anemia is a condition in which red blood cells are not able to carry adequate oxygen to the body's tissues, and is widely found in nearly a quarter of the world population. The typical method to screen for the iron-deficiency anemia, which is the major anemia found in the world, is to implement a blood test called a complete blood count (CBC). However, even though this test gives a highly accurate result, it requires an invasive blood drawing and lab analyzing which could potentially cause physical pain, high risk of infection and take a long time to analyze. Therefore, this research presents an alternative method using an optical technique to measure hemoglobin concentration, which is the common indicator for diagnosing anemia. The light absorbance of the oxyhemoglobin at the wavelength of 660 nm and the deoxyhemoglobin at the wavelength of 880 nm were measured using the MAX30100 sensor. These wavelengths of light are obtained from red and infrared (IR) LEDs. The concept is based on the different absorption coefficients of blood at different electromagnetic wavelengths. This fact is used to indirectly calculate the hemoglobin concentration of blood through the modified Beer-Lambert law. Moreover, the result has been further converted to absolute hemoglobin concentration using a calibration curve derived from the cyanmethemoglobin test, which is the regular method for hemoglobin determination. Besides, the android application was also provided which can wirelessly record or monitor the data. The experiment shows that an accuracy of 90.9% can be achieved by our proposed noninvasive method. Therefore, the noninvasive portable hemoglobin concentration monitoring by the optical sensor has an acceptable result when compared with the invasive method, with less pain and lower risk of infection, as well as shorter processing time. 10.3390/healthcare9060647
Optical absorbance-based rapid test for the detection of sickle cell trait and sickle cell disease at the point-of-care. Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy People afflicted with sickle cell disease (SCD) experience severe deterioration in quality of life. The disease is characterized by debilitating pain, anemia, and increased susceptibility to life threatening infections. This genetic disorder is endemic to many parts of the world. Extensive and accurate screening of individuals with sickle cell trait (SCT) in the population, coupled with genetic counselling can inhibit the propagation of the disease. The gold-standard techniques for the detection of sickle hemoglobin, such as capillary electrophoresis, HPLC, and genetic testing, are prohibitively expensive and time-consuming. Mass screening is usually conducted with a low-cost test called the solubility test, which does not offer high specificity. This study proposes a game-changing single-step low-cost method for rapidly yet accurately screening and diagnosing SCD and SCT. This method relies on the hitherto unexplored differences in the optical absorbance between diseased, trait, and normal blood samples, under deoxygenated conditions. The proposed method was tested in two phases of clinical validation: a pilot study and a blind study. A total of 438 patient samples were tested using the proposed method across the two phases. The proposed method offers an average accuracy, sensitivity, and specificity of 97.6%, 96.9%, and 98.6%, respectively. The proposed test has the potential to obliviate the conventional two-step process of screening and diagnostic tests as it can be used at the point-of-care with minimal training and yet yield results reliable enough to assess disability benefit claims. 10.1016/j.saa.2022.121394
A Multifunctional Microfluidic Device for Blood Typing and Primary Screening of Blood Diseases. Lin Jia-Hui,Tsai Tsung-Ting,Zeng Qiang,Chang Chun-Yen,Guo Jun-Yu,Lin Chi-Jui,Chen Chien-Fu ACS sensors In this work, we demonstrate a multifunctional, portable, and disposable microfluidic device for blood typing and primary screening of blood diseases. Preloaded antibodies (anti-A, anti-B, and anti-D) interact with injected whole blood cells to cause an agglutination reaction that blocks a microslit in the microfluidic channel to accumulate red blood cells and form a visible red line that can be easily read to determine the blood type. Moreover, the different blood density and agglutination properties of normal and subtype blood groups, as well as different blood diseases, including anemia and polycythemia vera, generate different lengths of blood agglutination within the channels, which allows us to successfully screen these various conditions in as little as 2 min. The required blood volume for each test is just 1 μL, which can be obtained by minimally invasive finger pricking. This novel method of observing agglutinated red blood cells to distinguish blood types and diseases is both feasible and affordable, suggesting its promise for use in areas with limited resources. 10.1021/acssensors.0c00969
A fast screening method for the detection of CERA in dried blood spots. Drug testing and analysis Continuous erythropoietin receptor activator (CERA) is a third-generation erythropoiesis-stimulating agent that was developed for the treatment of anemia. However, misuse of CERA for doping in endurance sports has been reported. Previous studies have shown blood as the matrix of choice for the detection of CERA, due to its high molecular weight. The use of dried blood spots (DBSs) for anti-doping purposes constitutes a complementary approach to the standard urine and venous blood matrices and could facilitate sample collection and increase the number of blood samples available for analysis due to reduced costs of sample collection and transport. Here, we investigated whether CERA could be indirectly detected in extracts of single DBSs using an erythropoietin-specific immunoassay that is capable of providing results within approximately 2 h. Reconstituted DBS samples were prepared from mixtures of red blood cell pellets and serum samples. The samples were collected in a previous clinical study in which six healthy volunteers were injected with a single, 200 μg dose of CERA. Using a commercially available ELISA kit, CERA was detected in the DBSs with a detection window of up to 20 days post-injection. Furthermore, in order to demonstrate the fitness-for-purpose, three authentic doping control serum samples, which were identified as containing CERA, were analyzed by the presented methodological approach on DBS. The testing procedure described here could be used as a fast and cost-effective method for the detection of CERA abuse in sport. 10.1002/dta.3142
Retinal vessel optical coherence tomography images for anemia screening. Chen Zailiang,Mo Yufang,Ouyang Pingbo,Shen Hailan,Li Dabao,Zhao Rongchang Medical & biological engineering & computing Anemia is a disease that leads to low oxygen carrying capacity in the blood. Early detection of anemia is critical for the diagnosis and treatment of blood diseases. We find that retinal vessel optical coherence tomography (OCT) images of patients with anemia have abnormal performance because the internal material of the vessel absorbs light. In this study, an automatic anemia screening method based on retinal vessel OCT images is proposed. The method consists of seven steps, namely, denoising, region of interest (ROI) extraction, layer segmentation, vessel segmentation, feature extraction, feature dimensionality reduction, and classification. We propose gradient and threshold algorithm for ROI extraction and improve region growing algorithm based on adaptive seed point for vessel segmentation. We also conduct a statistical analysis of the correlation between hemoglobin concentration and intravascular brightness and vascular shadow in OCT images before feature extraction. Eighteen statistical features and 118 texture features are extracted for classification. This study is the first to use retinal vessel OCT images for anemia screening. Experimental results demonstrate the accuracy of the proposed method is 0.8358, which indicates that the method has clinical potential for anemia screening. Graphical abstract. 10.1007/s11517-018-1927-8
A Low-Cost Test for Anemia Using an Artificial Neural Network. Computer methods and programs in biomedicine BACKGROUND:Anemia during pregnancy can complicate maternal and neonatal health and even lead to fatal consequences if not diagnosed early on. Around 99% of women who face maternal mortality are from middle or low-income countries. Early screening of anemia could facilitate improved health outcomes in pregnant women. Point of care techniques are preferred due to their ability to provide results rapidly and because they can be used by personnel with minimal or no training. Such techniques are especially useful in resource-constrained settings like rural parts of developing countries. OBJECTIVES:The aim of the study was to develop a tool using an Artificial Neural Network (ANN) to estimate hemoglobin values using color information recorded from blood sample images. Our method utilizes inexpensive consumables and a simple image acquisition setup that can be assembled easily. METHODS:This study explores a neural network model to estimate the hemoglobin content in an individual's blood sample. Blood samples were collected from 86 volunteers and the images of blood drops were obtained using an image acquisition setup designed by the team. The color intensity values calculated from the blood drop images were used as feature descriptors for the samples. The features obtained from our samples were consequently fed to the Artificial Neural Network. RESULTS:Our neural network that gives the best result has the architecture of 11 neurons in each of the 5 layers. The best model gave estimated hemoglobin levels by analyzing color of blood samples with an accuracy of ±1.8 g/dl Limits of agreement (LOA) and bias 0.03 g/dl (with mean error of 0.75 g/dl). The model was subsequently tested with a validation set prepared from an additional 65 samples. The estimated hemoglobin levels gave an accuracy of +2 g/dl to -1.9 g/dl Limits of agreement (LOA) and bias 0.06 g/dl (with mean error of 0.78 g/dl). CONCLUSION:Optimization of sensitivity and specificity has been able to achieve the sensitivity and specificity values as 95.5% and 52% respectively. These results are at par with the contemporary measurement techniques indicating that our method can be used as a workable screening technique itself. 10.1016/j.cmpb.2022.107251
Screening for anemia in pregnancy with copper sulfate densitometry. Pistorius L R,Funk M,Pattinson R C,Howarth G R International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics OBJECTIVE:The copper sulfate method of screening for anemia was evaluated to determine its accuracy in antenatal patients. METHODS:In an antenatal clinic in a tertiary referral center, which also serves a local urban black community, 100 antenatal patients were prospectively tested for anemia by Coulter hemoglobin analysis in comparison with the copper sulfate test. The respective accuracy and costs of the tests were evaluated. RESULTS:Once initial technical difficulties had been overcome, the copper sulfate test proved accurate in detecting a hemoglobin level < 10 g% in pregnancy (sensitivity 94%, specificity 95%, positive predictive value 80%, negative predictive value 99%). The cost of the copper sulfate test is estimated to be less than 0.3% that of the Coulter test. CONCLUSION:The copper sulfate test is accurate and inexpensive, and can be recommended for screening for anemia in pregnancy.
Anemia detection through non-invasive analysis of lip mucosa images. Frontiers in big data 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. 10.3389/fdata.2023.1241899
Smartphone-Integrated Label-Free Rapid Screening of Anemia from the Pattern Formed by One Drop of Blood on a Wet Paper Strip. ACS sensors Screening of anemic patients poses demanding challenges in extreme point-of-care settings where the gold standard diagnostic technologies are not pragmatic and the alternative point-of-care technologies suffer from compromised accuracy, prohibitive cost, process complexity, or reagent stability issues. As a disruption to this paradigm, here, we report the development of a smartphone-based sensor for rapid screening of anemic patients by exploiting the patterns formed by a spreading drop of blood on a wet paper strip wherein blood attempts to displace a more viscous fluid, on the porous matrix of a paper, leading to "finger-like" projections at the interface. We analyze the topological features of the pattern via smartphone-enabled image analytics and map the same with the relative occupancy of the red blood cells in the blood sample, allowing for label-free screening and classification of blood samples corresponding to moderate to severe anemic conditions. The accuracy of detection is verified by comparing with gold standard reports of hematology analyzer, showing a strong correlation coefficient () of 0.975. This technique is likely to provide a crucial decision-making tool that obviates delicate reagents and skilled technicians for supreme functionality in resource-limited settings. 10.1021/acssensors.2c00806
Microstrip isoelectric focusing with deep learning for simultaneous screening of diabetes, anemia, and thalassemia. Analytica chimica acta 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 R = 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. 10.1016/j.aca.2024.342696
Development of a Semaphore of Anemia: Screening Method Based on Photographic Images of the Ungueal Bed Using a Digital Camera. Hermoza Luis,De La Cruz Javier,Fernandez Elsa,Castaneda Benjamin Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Anemia is a disease present worldwide. High prevalence of anemia (43%) is found in the child population and its main long-term effect (slow cognitive development) can remain even if the disease has disappeared. One of the main reasons for the high prevalence of anemia in Peru is the poor screening coverage during the growth of the child due to the parents' fear of infringing pain on their children. We take advantage that anemia produces pallor in the hands, fingers and ungueal bed to develop a semaphore for this disease. This screening tool uses photographic images of the patient's ungueal bed to determine if they have a high, medium or low possibility of having anemia. Sixty people participated in the study and 6 photographic images for each participant's right hand were captured. The images were processed to extract regions of interest from each of the fingernails. Datasets were generated and a neural network was used to predict the risk of anemia. Initial results show that the proposed semaphore of anemia reaches a sensitivity of 0.79 and specificity of 0.91. These results indicate that the semaphore of anemia may be used as a screening method to reduce the number of blood tests and the time of evaluation from 15 minutes (rapid test with portable hemoglobinometer) to 1 minute. 10.1109/EMBC44109.2020.9176017
Comparison of biomarkers in blood and saliva in healthy adults. Williamson Sarah,Munro Cindy,Pickler Rita,Grap Mary Jo,Elswick R K Nursing research and practice Researchers measure biomarkers as a reflection of patient health status or intervention outcomes. While blood is generally regarded as the best body fluid for evaluation of systemic processes, substitution of saliva samples for blood would be less invasive and more convenient. The concentration of specific biomarkers may differ between blood and saliva. The objective of this study was to compare multiple biomarkers (27 cytokines) in plasma samples, passive drool saliva samples, and filter paper saliva samples in 50 healthy adults. Demographic data and three samples were obtained from each subject: saliva collected on filter paper over 1 minute, saliva collected by passive drool over 30 seconds, and venous blood (3 mL) collected by venipuncture. Cytokines were assayed using Bio-Rad multiplex suspension array technology. Descriptive statistics and pairwise correlations were used for data analysis. The sample was 52% male and 74% white. Mean age was 26 (range = 19-63 years, sd = 9.7). The most consistent and highest correlations were between the passive drool and filter paper saliva samples, although relationships were dependent on the specific biomarker. Correlations were not robust enough to support substitution of one collection method for another. There was little correlation between the plasma and passive drool saliva samples. Caution should be used in substituting saliva for blood, and relationships differ by biomarker. 10.1155/2012/246178
A Smartphone-Based Disposable Hemoglobin Sensor Based on Colorimetric Analysis. Sensors (Basel, Switzerland) Hemoglobin is a biomarker of interest for the diagnosis and prognosis of various diseases such as anemia, sickle cell disease, and thalassemia. In this paper, we present a disposable device that has the potential of being used in a setting for accurately quantifying hemoglobin levels in whole blood based on colorimetric analysis using a smartphone camera. Our biosensor employs a disposable microfluidic chip which is made using medical-grade tapes and filter paper on a glass slide in conjunction with a custom-made PolyDimethylSiloaxane (PDMS) micropump for enhancing capillary flow. Once the blood flows through the device, the glass slide is imaged using a smartphone equipped with a custom 3D printed attachment. The attachment has a Light Emitting Diode (LED) that functions as an independent light source to reduce the noise caused by background illumination and external light sources. We then use the RGB values obtained from the image to quantify the hemoglobin levels. We demonstrated the capability of our device for quantifying hemoglobin in Bovine Hemoglobin Powder, Frozen Beef Blood, and human blood. We present a logarithmic model that specifies the relationship between the Red channel of the RGB values and Hemoglobin concentration. 10.3390/s23010394
Rapid quantitative assays for glucose-6-phosphate dehydrogenase (G6PD) and hemoglobin combined on a capillary-driven microfluidic chip. Rocca Marco,Temiz Yuksel,Salva Marie L,Castonguay Samuel,Gervais Thomas,Niemeyer Christof M,Delamarche Emmanuel Lab on a chip Rapid tests for glucose-6-phosphate dehydrogenase (G6PD) are extremely important for determining G6PD deficiency, a widespread metabolic disorder which triggers hemolytic anemia in response to primaquine and tafenoquine medication, the most effective drugs for the radical cure of malaria caused by parasites. Current point-of-care diagnostic devices for G6PD are either qualitative, do not normalize G6PD activity to the hemoglobin concentration, or are very expensive. In this work we developed a capillary-driven microfluidic chip to perform a quantitative G6PD test and a hemoglobin measurement within 2 minutes and using less than 2 μL of sample. We used a powerful microfluidic module to integrate and resuspend locally the reagents needed for the G6PD assay and controls. We also developed a theoretical model that successfully predicts the enzymatic reactions on-chip, guides on-chip reagent spotting and allows efficient integration of multiple assays in miniaturized formats with only a few nanograms of reagents. 10.1039/d1lc00354b
Assessment of stored red blood cells through lab-on-a-chip technologies for precision transfusion medicine. Proceedings of the National Academy of Sciences of the United States of America Transfusion of red blood cells (RBCs) is one of the most valuable and widespread treatments in modern medicine. Lifesaving RBC transfusions are facilitated by the cold storage of RBC units in blood banks worldwide. Currently, RBC storage and subsequent transfusion practices are performed using simplistic workflows. More specifically, most blood banks follow the "first-in-first-out" principle to avoid wastage, whereas most healthcare providers prefer the "last-in-first-out" approach simply favoring chronologically younger RBCs. Neither approach addresses recent advances through -omics showing that stored RBC quality is highly variable depending on donor-, time-, and processing-specific factors. Thus, it is time to rethink our workflows in transfusion medicine taking advantage of novel technologies to perform RBC quality assessment. We imagine a future where lab-on-a-chip technologies utilize novel predictive markers of RBC quality identified by -omics and machine learning to usher in a new era of safer and precise transfusion medicine. 10.1073/pnas.2115616120
Emerging point-of-care technologies for anemia detection. Lab on a chip Anemia, characterized by low blood hemoglobin level, affects about 25% of the world's population with the heaviest burden borne by women and children. Anemia leads to impaired cognitive development in children, as well as high morbidity and early mortality among sufferers. Anemia can be caused by nutritional deficiencies, oncologic treatments and diseases, and infections such as malaria, as well as inherited hemoglobin or red cell disorders. Effective treatments are available for anemia upon early detection and the treatment method is highly dependent on the cause of anemia. There is a need for point-of-care (POC) screening, early diagnosis, and monitoring of anemia, which is currently not widely accessible due to technical challenges and cost, especially in low- and middle-income countries where anemia is most prevalent. This review first introduces the evolution of anemia detection methods followed by their implementation in current commercially available POC anemia diagnostic devices. Then, emerging POC anemia detection technologies leveraging new methods are reviewed. Finally, we highlight the future trends of integrating anemia detection with the diagnosis of relevant underlying disorders to accurately identify specific root causes and to facilitate personalized treatment and care. 10.1039/d0lc01235a
Label-free microfluidic cell sorting and detection for rapid blood analysis. Lab on a chip Blood tests are considered as standard clinical procedures to screen for markers of diseases and health conditions. However, the complex cellular background (>99.9% RBCs) and biomolecular composition often pose significant technical challenges for accurate blood analysis. An emerging approach for point-of-care blood diagnostics is utilizing "label-free" microfluidic technologies that rely on intrinsic cell properties for blood fractionation and disease detection without any antibody binding. A growing body of clinical evidence has also reported that cellular dysfunction and their biophysical phenotypes are complementary to standard hematoanalyzer analysis (complete blood count) and can provide a more comprehensive health profiling. In this review, we will summarize recent advances in microfluidic label-free separation of different blood cell components including circulating tumor cells, leukocytes, platelets and nanoscale extracellular vesicles. Label-free single cell analysis of intrinsic cell morphology, spectrochemical properties, dielectric parameters and biophysical characteristics as novel blood-based biomarkers will also be presented. Next, we will highlight research efforts that combine label-free microfluidics with machine learning approaches to enhance detection sensitivity and specificity in clinical studies, as well as innovative microfluidic solutions which are capable of fully integrated and label-free blood cell sorting and analysis. Lastly, we will envisage the current challenges and future outlook of label-free microfluidics platforms for high throughput multi-dimensional blood cell analysis to identify non-traditional circulating biomarkers for clinical diagnostics. 10.1039/d2lc00904h
Saliva-based microfluidic point-of-care diagnostic. Theranostics There has been a long-standing interest in point-of-care (POC) diagnostics as a tool to improve patient care because it can provide rapid, actionable results near the patient. Some of the successful examples of POC testing include lateral flow assays, urine dipsticks, and glucometers. Unfortunately, POC analysis is somewhat limited by the ability to manufacture simple devices to selectively measure disease specific biomarkers and the need for invasive biological sampling. Next generation POCs are being developed that make use of microfluidic devices to detect biomarkers in biological fluids in a non-invasive manner, addressing the above-mentioned limitations. Microfluidic devices are desirable because they can provide the ability to perform additional sample processing steps not available in existing commercial diagnostics. As a result, they can provide more sensitive and selective analysis. While most POC methods make use of blood or urine as a sample matrix, there has been a growing push to use saliva as a diagnostic medium. Saliva represents an ideal non-invasive biofluid for detecting biomarkers because it is readily available in large quantities and analyte levels reflect those in blood. However, using saliva in microfluidic devices for POC diagnostics is a relatively new and an emerging field. The overarching aim of this review is to provide an update on recent literature focused on the use of saliva as a biological sample matrix in microfluidic devices. We will first cover the characteristics of saliva as a sample medium and then review microfluidic devices that are developed for the analysis of salivary biomarkers. 10.7150/thno.78872
Microfluidic Systems for Blood and Blood Cell Characterization. Biosensors A laboratory blood test is vital for assessing a patient's health and disease status. Advances in microfluidic technology have opened the door for on-chip blood analysis. Currently, microfluidic devices can reproduce myriad routine laboratory blood tests. Considerable progress has been made in microfluidic cytometry, blood cell separation, and characterization. Along with the usual clinical parameters, microfluidics makes it possible to determine the physical properties of blood and blood cells. We review recent advances in microfluidic systems for measuring the physical properties and biophysical characteristics of blood and blood cells. Added emphasis is placed on multifunctional platforms that combine several microfluidic technologies for effective cell characterization. The combination of hydrodynamic, optical, electromagnetic, and/or acoustic methods in a microfluidic device facilitates the precise determination of various physical properties of blood and blood cells. We analyzed the physical quantities that are measured by microfluidic devices and the parameters that are determined through these measurements. We discuss unexplored problems and present our perspectives on the long-term challenges and trends associated with the application of microfluidics in clinical laboratories. We expect the characterization of the physical properties of blood and blood cells in a microfluidic environment to be considered a standard blood test in the future. 10.3390/bios13010013