View in EDS

Hough's Transform-Based IoT Device for Automated Identification and Prediction of Blood Groups.

Saved in:
Bibliographic Details
Title: Hough's Transform-Based IoT Device for Automated Identification and Prediction of Blood Groups.
Authors: Asokan, Vinoth, Swamy, Vignesh Ponnu, Baskaran, Sharmila
Source: Journal of Biomedical Physics & Engineering; Feb2026, Vol. 16 Issue 1, p71-80, 10p
Subject Terms: BLOOD groups, HOUGH transforms, BLOOD testing, IMAGE processing, INTERNET of medical things, ABO blood group system, BLOOD transfusion reaction, MEDICAL equipment
Abstract: Real-time data collection, sharing, and analysis of health-related information are made feasible using the Internet of Things (IoT) in the healthcare field. IoT could transform patient care, enhance clinical results, and optimize healthcare operations by integrating remote monitoring, automation, and data-driven decision-making. Determining the blood type is essential for safe blood transfusions, organ transplant compatibility, and preventing immunological responses. Additionally, the ABO blood group system prediction supports research on associations between blood types and various medical conditions, such as susceptibility to infections, cardiovascular diseases, and clotting disorders. Antigens (A and B) and the Rhesus (Rh) factor (+ or -) are usually used to determine blood grouping. By combining known antibodies with blood samples, the blood group can be examined by the agglutination reactions through image processing techniques. In this work, we proposed an intelligent portable blood analyser for blood type prediction and determination using an IoT-based system. The blood group identification and detection in blood samples is performed with a fabricated simulation device using a 3D Printer and acrylic materials. This system determines a solution using the adaptive Hough transform algorithm and provides the highest level of efficiency and accuracy in blood group identification and counting. Thus, the proposed system lowers the possibility of transfusion-related allergic responses and stores precise outcomes that exclude human-made errors, enabling us to instantly determine a person's blood type. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Biomedical Physics & Engineering is the property of Shiraz University of Medical Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Biomedical Index
Description
Abstract:Real-time data collection, sharing, and analysis of health-related information are made feasible using the Internet of Things (IoT) in the healthcare field. IoT could transform patient care, enhance clinical results, and optimize healthcare operations by integrating remote monitoring, automation, and data-driven decision-making. Determining the blood type is essential for safe blood transfusions, organ transplant compatibility, and preventing immunological responses. Additionally, the ABO blood group system prediction supports research on associations between blood types and various medical conditions, such as susceptibility to infections, cardiovascular diseases, and clotting disorders. Antigens (A and B) and the Rhesus (Rh) factor (+ or -) are usually used to determine blood grouping. By combining known antibodies with blood samples, the blood group can be examined by the agglutination reactions through image processing techniques. In this work, we proposed an intelligent portable blood analyser for blood type prediction and determination using an IoT-based system. The blood group identification and detection in blood samples is performed with a fabricated simulation device using a 3D Printer and acrylic materials. This system determines a solution using the adaptive Hough transform algorithm and provides the highest level of efficiency and accuracy in blood group identification and counting. Thus, the proposed system lowers the possibility of transfusion-related allergic responses and stores precise outcomes that exclude human-made errors, enabling us to instantly determine a person's blood type. [ABSTRACT FROM AUTHOR]
ISSN:22517200
DOI:10.31661/jbpe.v0i0.2410-1849