Detection Of Blood Group Research Articles

Blood Group Detection by Using Fingerprint

Before performing blood transfusions in severe situations, blood group detection is necessary. It is done before a blood transfusion in an emergency or when checking a person's blood group for donation. Currently, lab personnel perform tests manually in the laboratory. This takes time and may result in human mistake when determining blood type. The goal of the study survey is to use image processing to reduce the amount of physical labor required to identify blood groups. The presence or absence of agglutination reaction of blood with antigen will be used to determine the blood group

Blood Group Detection Using Image Processing

During medical crises, access to an ample blood supply is crucial for saving lives. Shortages of required blood types in hospitals can result in significant delays in patient treatment. To tackle this urgent issue, we introduce BloodHub, an extensive web platform designed to streamline blood search, availability assessment, and compatibility determination using Support Vector Machines (SVM). BloodHub acts as a centralized hub where both donors and recipients can register and participate in the blood donation process. The platform offers a user-friendly interface for individuals in need of blood donations, enabling them to locate specific blood types nearby. Additionally, BloodHub provides real-time updates on blood unit availability across registered blood banks and donation centers, simplifying the procurement process for healthcare facilities and emergency responders. One of BloodHub's standout features is its SVM- powered blood group detection capability. By examining genetic markers in blood samples, SVM algorithms accurately identify donors' blood groups, ensuring compatibility with recipient needs. This functionality not only improves blood-matching efficiency but also reduces the risk of transfusion-related complications. Moreover, BloodHub implements robust security measures to protect user privacy and confidentiality.

FPGA Based Efficient Detection of Blood Group and Hb-anemic Using Image Processing-VLSI Approach

During the critical situation of a patient, an immediate blood transfusion from others to a patient is necessary. The blood groups are AB, B, A and O. The identification of the blood group and Hb- anemic help to decide blood transfusion during emergencies. In order to check anaemia as well as to take complete blood count and to monitor a number of diseases, the hemoglobin test is an important one. But both tests are not possible in remote and village areas, because they require additional equipments and trained personnel which can be afforded only in lab. The conventional test methods involve the risk of infection, possibility of occurring human errors, more time consumption and noise interference. By addressing these limitations, an effective hardware system is proposed to detect blood group and Hbanemic by exploiting image processing mechanism with FPGA based VLSI process. The front-end model was developed using MATLAB software, where the input blood sample images were converted into gray-scale and perform GLCM model to generate the statistical parameters. The back-end model was developed using XILINX FPGA Spartan 6 kit/ Altera Quartus Ⅱ. The data with code is then fed to hardware kit to compare and correlate with the training samples using Stump Boost Regression Algorithm (SBRA). We considered World Health Organization (WHO)'s HCS method to detect Hb-anemic. The proposed system shows improved performance, optimal accuracy, less error rate and less time with low cost. This system can be used as a technician-free self-testing device, minimising the exposure of patients to clinical settings.

ABO Blood Grouping and Rhesus Factor Determination From Dental Pulp Tissue: A Forensic Research.

Introduction Collection of blood samples from mass disaster victims and blood grouping is a challenging task. This can be attributed to various reasons. From the forensic odontology perspective, teeth and bones are one of the noteworthy remainsthat can be derived from such sites. Aims and objectives The aim of our study is to detect ABO blood groups and Rhesus (Rh) factor from extirpated pulp tissue of the extracted teeth at zero, three, and six months' time interval by absorption-elution technique. Materials and methods The study consisted of 90 freshly extracted teeth as suggested by a biostatistician. Thirty teeth were analyzed immediately and 60 teeth were stored in vials without any preservative at room temperature. The pulp tissue was extirpated and studied at zero months, three months, and six months to determine blood groups and Rh factors. The extraction socket blood was tested to identify the blood group of that patient and used as a control reference. The blood grouping was done at respective time periods through the absorption-elution method and matched with the control. Data were analyzed using Statistical Package for the Social Sciences (SPSS) version 24 software (IBM Corp., Armonk, NY). The chi-square test and Kruskal-Wallis test were done. A p-value< 0.05 was considered statistically significant. Results Pulp showed the highest sensitivity for blood groups at zero months but it could be identified up to six months, although the sensitivity and specificity gradually decreased. ABO blood grouping showed higher sensitivity than the Rh factor as time progressed. Conclusion In cases where teeth are the only remains in a forensic condition, the dental pulp can be an authentic source for blood group detection.

Blood Group Detection through Finger Print Images using Image Processing

Blood Group Detection through Finger Print Images using Image Processing

Establishment and application of suspension static method in blood group screening of automated blood group analyzer

The accuracy of blood group identification is the basis of blood transfusion safety. In order to increase the detection rate of weak agglutination, unexpected antibodies (UAb) and blood subtypes for pre-transfusion testing, the blood group screening process of automated blood group analyzer (ABGA) is ameliorated by introducing one static step and establishing a suspension static method (SSM). One static step was introduced in the blood group screening process of ABGA and three static time conditions were designed: 300 s, 400 s and 500 s, from which the optimal static time was selected and SSM was established; By comparing the detection of weak agglutination and UAb before and after the application of SSM, the feasibility and effect of suspension static method were verified and evaluated. The last two steps of the automatic blood group screening process were replaced with static, light centrifugation and imaging. The optimal static time parameter was selected as 400 s and SSM was established; After the application of SSM, it was verified that: (1) The detection level of weak antibodies (anti-A and anti-B) and weak antigens (weak D phenotype) could be improved by SSM, including antibodies in plasma of known type O samples with 0, 2, 4, 8, 16 and 32 times serial dilutions(simulating weak anti-A and weak anti-B), weak antibodies (anti-B) in plasma of one normal A-type sample and weak antigens on red blood cells (RBC) of 5 weak D phenotype samples (weak D antigen); (2) Three blood donor samples (type A, O and B) with known UAb were detected by SSM. The results showed that SSM could detect both weak antibodies (anti-A and anti-B) and UAb; (3) SSM was applied to detect the samples of 3 A2B and 3 subtype B blood donors and the blood subtypes could be clearly detected; (4) The number of screening samples was 95,314 and 106,814 before SSM (2018) and after (2020) the application of SSM and the positive rate of UAb (63/95,314 and 187/106,814) increased after SSM, discrepancy of which was statistically significant (χ2 = 48.42, P < 0.01). The above results demonstrate that SSM of ABGA is conducive to the detection of weak agglutination, UAb and blood subtypes in blood samples, which can improve the sensitivity of blood group detection and ensure the safety of clinical blood transfusion to a certain extent.

ABO Blood group Detection in Teeth from Cadaver: A Modified Absorption-Elution Technique

In a medicolegal investigation, a forensic pathologist, sometimes known as a forensic dentist, works hand in hand with a police officer to determine an individual's identification as a source of information. The blood group might be essential in establishing the victim's identification and the culprits who committed the crime in medico-legal investigations. Because dental tissues are hard of all human tissues and may be conserved for a long time even after death, they are significant for forensic purposes. The main goal of this study was to use the Adsorption-Elution method to detect blood group and Rhesus factor from soft and hard tissue of the tooth as biological evidence and also determine the ABO blood grouping from patients and cadavers in forensic investigation. The results showed, that there was a strong positive matching of the blood grouping with the coagulation of the tooth specimen.

Survey on Non-Invasive Blood Group Detection Using Image Processing

Detecting the blood group of people is necessary, before performing a transfusion of blood in an urgent situation or when checking a person’s blood group for donation. Presently, the tests are conducted manually by the lab personnel in the laboratory. This takes time and consequences in human error when determining blood type. This may result in loss of life or seriously affect people’s lives. To overcome this, we propose a non-invasive model using image processing and machine learning algorithms. Image processing is useful in various ways for researchers to succeed in their goals, especially in medical fields. This system displays the corresponding blood type that is A+, A-, B+, B-, AB+, AB-, O+, and O- in a short time. Image matching techniques like ORB are used to extract features from an image. It involves signal extraction processes, spectroscopic information, feature calculation processes, and Photoplethysmographic (PPG) signal. The features extracted are given to a machine learning model to detect the blood type of a person in a short period. This method does not require mixing any antigens like A, B, and O with blood samples. This method does not use any blood samples, does not require any chemicals, and reduces human error.

Effect of clinical application of anti-CD38 and anti-CD47 monoclonal antibodies on blood group detection and transfusion therapy and treatment

BackgroundTo investigate the effect of anti-CD38 monoclonal antibodies (mAb) (daratumumab, DARA) and anti-CD47 mAb combined with azacytidine on blood transfusion compatibility tests, transfusion effects in the treatment of multiple myeloma or acute myeloid leukemia and the corresponding management strategy. Materials and methodsAmong the 19 patients who were treated with DARA and anti-CD47 mAb, 4 patients with cross matching incompatibility were selected. The ABO blood group, the Rh blood group, irregular antibody screening and direct antiglobulin test (DAT) and cross matching testing were performed before and after the application of mAbs using serological methods. Then, irregular antibody screening and microcolumn gel cross matching tests were performed with donor and recipient erythrocytes and serum treated with DL-dithiothreitol (DTT) and Immucor kit, respectively. The transfusion effect was monitored. Results21.05% (4/19) patients had mismatched cross-matching results after mAb treatment. The agglutination intensity of irregular antibody screening tests (3 + ∼ 4 +) after anti-CD47 mAb was higher than that (1 + ∼ 2 +) after DARA. In the DARA group, treating RBCs with 0.2 mol L−1 DTT eliminated the DARA interference with antibody screening. In the anti-CD47 mAb group, the antibody screening, cross-matching test and DAT had been strongly interfered, and using Immucor kit eliminated the interference with antibody screening testing. There was no difference in the transfusion effect. ConclusionThe application of mAb drugs led to incompatibility of cross matching tests, and the transfusion effect was not affected.

Blood group detection using ML classifier

Determining blood types is very important during emergency situations before giving a blood transfusion. Currently, tests that are performed manually by technicians can lead to human error. As the tests are performed manually, if an inappropriate blood group is detected it can result in the death of an individual. Blood type determination in a short time and without human error is very essential. Many methods are employed which aims at determining the type of blood. The first method developed is determination of blood type using fiber optics. In this method, optical signals are fed into blood sample depending upon the optical variations of different blood groups the corresponding blood group is detected. But, this method fails to find the Rh (positive and negative) type of blood group. The second method employed is classification of blood type by microscopic images. Initially image processing is performed by histogram equalization, and then corresponding blood group is analyzed by Quantification Techniques. In this Technique, the major disadvantage lies in the inaccurate detection of agglutination. In order to avoid these disadvantages a new method Blood group detection using an ML classifier, Support Vector Machine (SVM) is proposed.

Blood Group Classification System Based on Image Processing Techniques

The present paper proposes a novel system that automatically classifies the eight different blood groups according to the ABO and Rh blood group systems. The proposed system is developed by applying MATLAB’s image processing techniques on the blood sample images. These images are acquired from the laboratory using the slide test. It utilizes a mean filter for removing noise from blood sample images. In addition, the Contrast Limited Adaptive Histogram Equalization (CLAHE) is used for enhancing the image characteristics analysis. The proposed system also utilizes the automated threshold strategy (Otsu’s approach) for obtaining the blood samples binary images. Since, adding the three antigens (A, B, D) to the blood samples works on clumping the samples or not, and this, in turn, works on the variation number of objects and holes in the different blood groups’ samples. Therefore, the “Bwboundaries” Matlab function is applied for the first time in the proposed system to count the number of objects and holes for blood group classification. The database used in the proposed system consists of 600 images collected from different laboratories. The suggested system has many advantages such as simplicity, speeding up processing time, reducing human interference and errors, decreasing the risk of transfusion reactions, and high accuracy, even in remote places. As well, it can be used by a lab technician or any user who doesn’t have any previous knowledge of the blood group detection technique. In addition to using this system to assist in several applications such as premarital tests, blood transfusion as well as paternity tests.

Detection and Implementation of Blood Group and Hb Level by Image Processing Techniques

In an emergency, an urgent blood transfusion from a person to the patient is required and blood group identification is the first process to do so. In addition, a hemoglobin test is often required to make decisions about blood transfusion as well as to check anemia. Hemoglobin testing is also required for complete blood count and monitoring a number of diseases. These blood tests are almost difficult in rural areas where lab facilities are not sufficient. Researchers proposed a number of methods to identify blood groups using computer vision techniques. However, no study was conducted to identify blood group and hemoglobin level in a work using image processing techniques and an android mobile application which shows high detection accuracy. In this paper, manual clinical experiments have been replaced by an android app using image processing techniques to detect blood groups and hemoglobin levels except users require using antigen before taking samples. The proposed technique is divided into two portions. The first portion is blood group detection, which is done by taking a blood sample and performing the grayscale conversion, binary conversion, segmentation, edge detection, and computation to make the decision. The second section describes how to determine hemoglobin levels by comparing a blood sample image to a hemoglobin color scale (HCS). Here, the Hemoglobin value is determined from their RGB values. It has been discovered that the proposed approaches are capable of detecting hemoglobin levels and blood groups in a cost-effective and error-free manner. As a result, the tests can be conducted in a remote area without adequate lab facilities and the proposed work can solve major steps in blood transfusion difficulties and anemia.

Blood Group Detection using Image Processing MATLAB

Blood grouping is one of the common and most essentiality for many of the major healthcare applications. Traditional way to determine the blood group involve human such as trained medical professionals which generally lead to human error. One of the solutions to overcome this issue is to automate and digitize this method. Image processing and computer vision techniques can be used for this purpose. Therefore, in this paper, we investigate the blood group detection using image processing techniques. For this purpose, experiment starts by taking images of sample blood slide as input and convert it into gray scale followed by binarization and canny edge detection. Finally, it decided the agglutination by counting detected edges. Performance of method is tested on real- time blood sample dataset. Experimental results show the accuracy of proposed method is comparable to real- time test.

A Survey on Identification of Blood Group Types and its Hemoglobin Estimation Using Image Processing

It is very crucial to determine human blood groups in an emergency situation. But according to current system, the detection procedure is very slow. At present, human blood groups are determined manually through plate test procedure. It consists of blood collection and mixing with specific reagents in order to determine the blood agglutination. Blood typing is system which basically used to detect specific blood group of human. Investigation of appropriate blood type within short interval of time plays vital role in blood transfusion, donation, accident and other emergency situations. Currently, blood typing tests are performed by technicians in laboratories. But it is monotonous to do with large number of blood samples, require more time to diagnosis and, as these tests carried out manually, it may introduce human error which will be harmful. Hemoglobin (Hb), a very significant parameter for the human body and deficiency of it causes anemia. During pregnancy, menstruation and ICU deficiency of it can be very risky and even caused death. So, it is important to diagnose it continuously. Usually, physicians examine it by conducting a blood test to confirm it is painful, time-consuming and costly. The major concept of this study is to screen Hb levels within a short period of time. This paper focused on recent advances in detection of blood group with hemoglobin, which can classify different blood group within short period of time with great accuracy and calculate its hemoglobin level.

ABO blood group detection in extracted teeth: A forensic study

Aim: The aim of this study was to ascertain ABO blood group from extracted teeth using pulp and dentin tissues with the help of the absorption–elution (AE) technique. Materials and Methods: The study was conducted using an experimental study design and included 60 patients who underwent extraction due to periodontal and therapeutic purposes. Blood group antigens were ascertained for all the study participants using capillary blood by slide agglutination method (Controls). AE technique was used to check blood grouping using powdered dentine and dental pulp immediately after extraction and after 9 months. The study group was compared with the control group for blood group determination at different time intervals to find the sensitivity of dental pulp and a significant difference between those values at different time intervals. The statistical tests used were the Shapiro–Wilk test, chi-squared test, multivariate linear regression, and the Pearson correlation coefficient. Results: A total of 60 study subjects, 39 males and 21 females, were taken. In the estimation of blood group, 54 teeth, that is, 90% of total sample, were positive. We found an inverse relationship between the result for the blood grouping and time intervals, that is, 100% and 80% test results, done on the day of extraction and after 9 months. Conclusion: It could be inferred that the antigens from pulp are biologically stable for long time. This study brings a spotlight on the time duration for which teeth can remain as the prominent source for the detection of blood group.

Numerical investigation of an optimized plasmonic on-chip refractive index sensor for temperature and blood group detection

A highly sensitive sensor focused on two Metal-Insulator-Metal (MIM) waveguides and three quadrilateral cavities sandwiched perpendicularly in between the MIM waveguides is proposed. Fano resonance induced by the coherent superposition of the narrow band spectral response and broadband spectral response, excites the structural transmission characteristics. The Finite Element Method (FEM) is used to numerically investigate the transmission characteristics and sensitivity of the refractive index sensor for different configurations. The linear relationship between resonant wavelength and refractive index is used to sense the materials. By optimizing the structural parameters, a numerical evaluation of the refractive index sensitivity (S) up to 1556 nm/RIU, and associate figure of merit (FOM) of 14.83 is recorded. The device is also explored as a temperature sensor with 0.61 nm/°C sensitivity. Since ethanol is used as a sensing medium, the operating range of the sensor is between −114.3 °C and 78 °C, melting and boiling temperature of ethanol. To detect human blood groups using the proposed sensor, a mathematical model is developed, which shows impressive performance for the detection of human blood groups very precisely. Compact size, ultrafine sensitivity, and sharp Fano peak profile make the proposed sensor an ideal candidate for on-chip sensors.

Identification of Aptamers that Specifically Bind to A1 Antigen by Performing Cell-on Human Erythrocytes

Background: The apply of aptamers as a new generation’s way to probe diagnostic for the detection of target molecules has gained ground. Aptamers can be used as alternatives to diagnostic antibodies for detection of blood groups due to their unique features. This study was aimed to produce DNA diagnostic aptamer detecting the antigen of A1 blood group using the Cell-Selex method. Materials and Methods: DNA aptamer was isolated against A1 RBC antigen after ten stages of Cell-Selex and amplification by an asymmetric polymerase chain reaction. The progress of the stages of selection was evaluated using flow cytometry analysis, which the DNA aptamer isolated from the tenth cycle with an affinity of 70% fluorescent intensity, was selected from four positive colonies followed by determination of the sequences and secondary structures. Results: The aptameric sequence obtained from C4 cloning was calculated with the highest binding affinity to A1 antigen having an apparent dissociation constant (Kd value) of at least 29.5 ± 4.3 Pmol, which was introduced as the selected aptamer-based on ΔG obtained from a colony of C4 equal to –13.13. Conclusion: The aptamer obtained from using Cell-Selex method could be used as an example for the development of diagnostic tools such as biosensors for detecting A1 blood group antigens. [GMJ.2020;9:e1657]

Identification of Aptamers that Specifically Bind to A1 Antigen by Performing Cell-on Human Erythrocytes.

Background: The apply of aptamers as a new generation’s way to probe diagnostic for the detection of target molecules has gained ground. Aptamers can be used as alternatives to diagnostic antibodies for detection of blood groups due to their unique features. This study was aimed to produce DNA diagnostic aptamer detecting the antigen of A1 blood group using the Cell-Selex method. Materials and Methods: DNA aptamer was isolated against A1 RBC antigen after ten stages of Cell-Selex and amplification by an asymmetric polymerase chain reaction. The progress of the stages of selection was evaluated using flow cytometry analysis, which the DNA aptamer isolated from the tenth cycle with an affinity of 70% fluorescent intensity, was selected from four positive colonies followed by determination of the sequences and secondary structures. Results: The aptameric sequence obtained from C4 cloning was calculated with the highest binding affinity to A1 antigen having an apparent dissociation constant (Kd value) of at least 29.5 ± 4.3 Pmol, which was introduced as the selected aptamer-based on ΔG obtained from a colony of C4 equal to –13.13. Conclusion: The aptamer obtained from using Cell-Selex method could be used as an example for the development of diagnostic tools such as biosensors for detecting A1 blood group antigens.

Blood Group Measurement using Light Emitting Diode

Blood grouping is basically the categorization of blood based on the presence or absence of antigens and antibodies present on the surface of the Red Blood Cells (RBC’s).The most common and most important type of blood type classification system is ABO system and the Rh system. The ABO system includes blood types like A, B, AB, and O group. This identification system plays a major role during transmission and transfusion of blood. The major risk involved in blood transfusion is getting the wrong blood type by accident. A recent survey shows that for every 1 million units of blood transfused, getting the wrong blood type happens, at the most, 4 times. Transfusion with the wrong blood type can lead to a severe reaction that may be life-threatening. The existing method involves a standard procedure in which blood is drawn from the patient’s body by pricking the finger with a needle or by using a syringe. The shortcomings of this method are addition of chemical reagents, hassle for patients and time of operation is more. The present proposal involves a prickless technique for blood group detection. This proposal presents a novel method for non invasive and instantaneous blood group measurement using the transmittance and reflectance property of light emitting diode. The proposed method aims to design a small and compact prototype which would be efficient and effective than the existing methods. Moreover it would be designed at a very low cost and provide comfort to the patients as well.

Engineering Hexagonal Array of Nanoholes for High Sensitivity Biosensor and Application for Human Blood Group Detection

In this study, the design of refractive index sensor with high sensitivity would be provided. This consists of a metal substrate with two metal-insulator-metal waveguide and an array of hexagonal nanoholes. Using the refractive index model obtained for different blood groups A, B, and O, we show that the structure can be used as a sensor to determine a human blood group. We used the array of nanoholes due to the unique optical properties, which leads to nanoscale confinement, high sensitivity to surface, and low propagation losses. Based on the results, resonance wavelength has a linear relationship with the refractive index of a material that is placed inside the nanohole; this feature makes it easy to identify the material. Considering a tradeoff between the transmitted power, structure size, and sensitivity, finite-difference time-domain simulations show that the sensitivity can be as large as 3172 nm per refractive index unit. In general, our plasmonic sensor can promote the sensitivity through the phenomenon of plasmon exciting on the surface of the nanoholes and would have useful applications in the medical field such as determining blood group, hemoglobin, and deoxyribonucleic acid (DNA) quantification.