Journal of Biomedical Physics and Engineering最新文献

{"title":"The Influence of Harmonic Ratio on HIFU-Induced Thermal Lesion Formation in Biological Tissue.","authors":"Hu Dong, Jiwen Hu, Wei Chen","doi":"10.31661/jbpe.v0i0.2502-1889","DOIUrl":"10.31661/jbpe.v0i0.2502-1889","url":null,"abstract":"<p><strong>Background: </strong>High-Intensity Focused Ultrasound (HIFU) is a non-invasive therapeutic technique, widely used for thermal ablation of tumors and other pathological tissues. The formation of thermal lesions during HIFU therapy is influenced by nonlinear acoustic effects, including the generation of harmonics and harmonic ratios.</p><p><strong>Objective: </strong>This study aimed to investigate the influence of the harmonic ratio on HIFU-induced thermal lesion formation in biological tissue. A coupled acoustic-thermal model is developed to simulate HIFU wave propagation and temperature elevation, incorporating nonlinear acoustic effects and dynamic tissue properties.</p><p><strong>Material and methods: </strong>This numerical study investigates the influence of the harmonic ratio (<i>R^*</i> =(<i>P₃</i> +<i>P₄</i> )/<i>P₂</i> ) on HIFU-induced thermal lesion formation in liver tissue. A coupled acoustic-thermal model was developed to simulate the propagation of HIFU waves and the resulting temperature elevation in tissue. The model incorporates nonlinear acoustic effects and dynamic tissue properties. The relationship between harmonic ratio, focal temperature, and thermal lesion size was analyzed through numerical simulation.</p><p><strong>Results: </strong>The results demonstrate that the harmonic ratio significantly affects the spatial distribution of acoustic energy deposition and the resulting thermal lesion size. Specifically, below 50 °C, the harmonic ratio decreases with rising focal temperature; above 50 °C, the harmonic ratio increases, indicating a turning point at the thermal denaturation threshold of liver tissue. Furthermore, higher harmonic ratio values correlate with larger thermal lesions, suggesting that higher-harmonic components enhance energy deposition in the focal region.</p><p><strong>Conclusion: </strong>These findings provide insights into optimizing HIFU therapy by controlling harmonic ratio generation, thereby improving treatment efficacy and safety.</p>","PeriodicalId":38035,"journal":{"name":"Journal of Biomedical Physics and Engineering","volume":"16 2","pages":"109-130"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13122350/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147784076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Concerns Regarding the Efficacy of Utilizing the Vibrational Response of Aminocyanine Molecules to Near Infrared Light for Cancer Cell Destruction.","authors":"Parsa Faghani-Eskandarkolaei, James Welsh, Seyed Ali Reza Mortazavi, Parmis Taghizadeh, Mobina Baramaki, Fatemeh Shams, Mohammed Nasir Abubakari, Seyed Mohammad Javad Mortazavi, Lembit Sihver","doi":"10.31661/jbpe.v0i0.2408-1806","DOIUrl":"10.31661/jbpe.v0i0.2408-1806","url":null,"abstract":"<p><p>Recent advancements in cancer treatment have introduced the use of aminocyanine molecules, activated by near-infrared (NIR) light, to induce vibrational responses that can selectively destroy cancer cells. This commentary critically examines a study that reports a 99% efficacy of this method against human melanoma cells in vitro, and significant tumor reduction in murine models. While the findings are promising, our analysis highlights crucial oversights in the study's implications for clinical applications. Specifically, the persistence of even a small fraction of cancer cells post-treatment poses significant risks for tumor regrowth and acquired resistance. Additionally, the study's approach neglects the heterogeneity of cancer cells and the presence of cancer stem cells, which are known to contribute to recurrence and resistance. We also discuss the limitations of the Tumor Control Probability (TCP) model in predicting treatment outcomes, emphasizing that achieving near-total eradication of cancer cells is necessary to prevent recurrence. Our commentary underscores the need for comprehensive research to address these challenges and ensure the efficacy and safety of novel cancer treatments utilizing aminocyanine molecules and NIR light.</p>","PeriodicalId":38035,"journal":{"name":"Journal of Biomedical Physics and Engineering","volume":"16 2","pages":"165-168"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13122340/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147784118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Astronaut Resilience: The Role of Elevated ROS in Adapting to Space Radiation.","authors":"Mahsa Pakroo, Seyed Ali Reza Mortazavi, Seyed Mohammad Javad Mortazavi","doi":"10.31661/jbpe.v0i0.2407-1791","DOIUrl":"10.31661/jbpe.v0i0.2407-1791","url":null,"abstract":"<p><p>The microgravity environment and high radiation levels in space lead to a significant increase in Reactive Oxygen Species (ROS) production compared to Earth, which can have detrimental effects on astronaut health over time. This study examines the hypothesis that high levels of ROS in living organisms in space may aid pre-selected astronauts' cells in adapting to the intense radiation encountered during missions to Mars and beyond. By looking at evolutionary biology and past radiation events like the Chernobyl disaster, we suggest that increased ROS could trigger adaptive responses similar to those seen in radiation-resistant organisms such as tardigrades. This paper explores the dual nature of ROS as both harmful agents and vital signaling molecules, evaluating their potential to enhance DNA repair, boost antioxidant defenses, and alter mitochondrial metabolism. We aim to see if managing ROS could be a strategy to prepare astronauts' cells for space travel, using cytogenetic tests to find individuals with strong adaptive responses.</p>","PeriodicalId":38035,"journal":{"name":"Journal of Biomedical Physics and Engineering","volume":"16 1","pages":"81-84"},"PeriodicalIF":0.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12884056/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146158761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Margins of Planning Target Volume and Set-up Errors based on Megavoltage Cone-Beam CT Image Guided Radiotherapy with Helical Tomotherapy: Importance of Automatic Registration Plus Manual Registration.","authors":"Danial Seifi Makrani, Nooshin Banaee, Ghazale Geraily, Hassan Ali Nedaie, Alireza Khorrami Moghaddam, Hussam Hameed Jassim","doi":"10.31661/jbpe.v0i0.2403-1734","DOIUrl":"10.31661/jbpe.v0i0.2403-1734","url":null,"abstract":"<p><strong>Background: </strong>Helical Tomotherapy (HT) enables daily verification of patient positioning using Megavoltage Computed Tomography (MVCT) during each treatment session.</p><p><strong>Objective: </strong>The present study aimed to investigate the effects of Automatic Registration (AR) compared to a combination of Automatic and Manual Registration (AR+MR) on setup errors. Additionally, the study aimed to determine the corresponding Margins of the Planning Target Volume (MPTV).</p><p><strong>Material and methods: </strong>In this experimental study, a total of 1513 daily MVCT scans were analyzed from September 2020 to January 2024, which were obtained from 71 patients diagnosed with Head and Neck (HN), cervical, and gastrointestinal cancer. The scans were registered with the planning CT to determine the setup errors of the patients. The analysis compares the setup errors between the AR and the AR+MR techniques in translational (X, Y, and Z axes) and rotational directions (R_X, R_Y, and R_Z). Additionally, the study calculated the MPTV.</p><p><strong>Results: </strong>In the AR and AR+MR techniques, the translational setup errors were significantly different in the Z-axis for HN patients. For cervical cancer patients, AR and AR+MR exhibited significantly different translational errors across all axes. Furthermore, they also had notable differences in the Y and Z-axis translational errors for Gastro-Intestinal (GI) patients. Regarding the rotational setup errors, a substantial difference was observed in the Z-axis translational error for cervical cancer patients, and in the Y and Z-axes for GI patients.</p><p><strong>Conclusion: </strong>Human assessment after automatic registration helps ensure that the registration is clinically appropriate, especially in circumstances involving deformable patient anatomy.</p>","PeriodicalId":38035,"journal":{"name":"Journal of Biomedical Physics and Engineering","volume":"16 1","pages":"57-70"},"PeriodicalIF":0.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12883926/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146158811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Supervised Learning Algorithm Comparison in Discharge Status Prediction of Trauma Patients: Empirical Evaluation.","authors":"Zahra Kohzadi, Ali Mohammad Nickfarjam, Zeinab Kohzadi, Leila Shokrizadeh Arani, Mehrdad Mahdian, Felix Holl","doi":"10.31661/jbpe.v0i0.2308-1654","DOIUrl":"10.31661/jbpe.v0i0.2308-1654","url":null,"abstract":"<p><strong>Background: </strong>By analyzing information from trauma centers, hospitals can identify crucial performance indicators that affect budgets and present growth opportunities, potentially leading to lower mortality rates and improved health status indicators.</p><p><strong>Objective: </strong>This study aims to determine the best-supervised algorithm for diagnosing the discharge status of trauma patients.</p><p><strong>Material and methods: </strong>This retrospective study used the data, collected by the Kashan Trauma Registry from March 2018 to February 2019. Several supervised algorithms, including Naive Bayes, Logistic Regression, Support Vector Machine, Random Forest, and K-Nearest Neighbors, have been evaluated for predicting the discharge status of trauma patients. The performance metrics of accuracy, precision, recall, and F-measure were used. The hold-out technique was applied to train the data.</p><p><strong>Results: </strong>The Random Forest algorithm had the best performance among the other algorithms. The best accuracy, precision, recall, and F-measure for Gini index were 84/2%, 79/7%, 78/3%, and 76.4%, and for information gain were 84.6%, 79.6%, 76.8%, and 76/20%, respectively.</p><p><strong>Conclusion: </strong>The results of this research showed that the supervised algorithms, with proper parameter settings, can help diagnose the discharge status of trauma patients. In addition, data balancing can help improve the performance of the algorithms. However, this claim cannot be generalized because it depends on the type of algorithm and the values of the parameters.</p>","PeriodicalId":38035,"journal":{"name":"Journal of Biomedical Physics and Engineering","volume":"16 1","pages":"33-46"},"PeriodicalIF":0.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12883923/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146158824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Decision Support System for Managing Health Symptoms of Living Near Mobile Phone Base Stations.","authors":"Hossein Parsaei, Mehdi Faraz, Seyed Mohammad Javad Mortazavi","doi":"10.31661/jbpe.v0i0.2310-1667","DOIUrl":"10.31661/jbpe.v0i0.2310-1667","url":null,"abstract":"<p><strong>Background: </strong>The rapid increase in the number of Mobile Phone Base Stations (MPBS) has raised global concerns about the potential adverse health effects of exposure to Radiofrequency Electromagnetic Fields (RF-EMF). The application of machine learning techniques can enable healthcare professionals and policymakers to proactively address concerns surrounding RF-EMF exposure near MPBS.</p><p><strong>Objective: </strong>The current study aimed to investigate the potential of machine learning models for the prediction of health symptoms associated with RF-EMF exposure in individuals residing near MPBS.</p><p><strong>Material and methods: </strong>This analytical study utilized Support Vector Machine (SVM) and Random Forest (RF) algorithms, incorporating 11 predictors related to participants' living conditions. A total of 699 adults participated in the study, and model performance was assessed using sensitivity, specificity, accuracy, and the Area Under Curve (AUC).</p><p><strong>Results: </strong>The SVM-based model demonstrated strong performance, with accuracies of 85.3%, 82%, 84%, 82.4%, and 65.1% for headache, sleep disturbance, dizziness, vertigo, and fatigue, respectively. The corresponding AUC values were 0.99, 0.98, 0.920, 0.89, and 0.81. Compared to the RF model and a previously developed model, the SVM-based model exhibited higher sensitivity, particularly for fatigue, with sensitivities of 70.0%, 83.4%, 85.3%, 73.0%, and 69.0% for these five health symptoms. Particularly for predicting fatigue, sensitivity and AUC were significantly improved (70% vs. 8% and 11.1% for SVM, Multilayer Perceptron Neural Network (MLPNN), and RF, respectively, and 0.81 vs. 0.62 and 0.64, for SVM, MLPNN, and RF, respectively).</p><p><strong>Conclusion: </strong>Machine learning methods, specifically SVM, hold promise in effectively managing health symptoms in individuals residing near or planning to settle in the vicinity of MPBS.</p>","PeriodicalId":38035,"journal":{"name":"Journal of Biomedical Physics and Engineering","volume":"16 1","pages":"47-56"},"PeriodicalIF":0.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12883925/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146158720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of Dose Distribution at the Level of Electronic Portal Imaging Device using Treatment Planning System.","authors":"Amene Bagheri, Mehdi Momennezhad, Shahrokh Nasseri, Azam Eskandari, Erfan Saatchian, Maryam Naji, Mohammad Yasin Mohammadi","doi":"10.31661/jbpe.v0i0.2312-1700","DOIUrl":"10.31661/jbpe.v0i0.2312-1700","url":null,"abstract":"<p><strong>Background: </strong>Treatment Planning Systems (TPS) are designed to calculate dose distributions within the CT imaging field of view. However, the Electronic Portal Imaging Device (EPID) is positioned outside this area, making it challenging to use standard TPS for dose calculations at the EPID level.</p><p><strong>Objective: </strong>The objective of this study is to present an innovative approach to address the limitations of TPS in calculating dose distribution at the EPID level.</p><p><strong>Material and methods: </strong>In this retrospective quantitative study, the CT image was extended to the EPID level and imported into the TPS. 42 treatments were planned, and doses were calculated. The TPS doses were then compared with the measured doses obtained using an Ion Chamber (IC). The study also investigated the impact of field size, phantom thickness, and air gap for energies of 6, 10, and 15 MV.</p><p><strong>Results: </strong>The average, minimum, and maximum dose differences were 1.91%, 0.02%, and 5.79% when changing the field size from 5×5 cm² to 20×20 cm², 3.62%, 0.18%, and 6.91% when the phantom thickness changed from 10 to 30 cm, and 3.5%, 0.4%, and 7.46% when the air gap was varied from 30 to 60 cm respectively. 97% of all changes in IC values can be predicted through the linear relationship with TPS.</p><p><strong>Conclusion: </strong>The validated proposed method in this study, as an innovative approach, effectively addresses the limitation of TPS in calculating dose distribution at the EPID level. This can be used as a reference for comparing the measured dose obtained by EPID in dosimetric verification.</p>","PeriodicalId":38035,"journal":{"name":"Journal of Biomedical Physics and Engineering","volume":"16 1","pages":"21-32"},"PeriodicalIF":0.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12883921/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146158740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Selenium-Curcumin-PEG Nanoparticles Radiosensitization for Intensity-Modulated Radiation Therapy of Lung Tumor Cells: <i>In Vitro</i> Synergistic Combination Therapy.","authors":"Farid Mortazavi, Paria Tamaddon, Ali Ketabi, Hanieh Haghighi, Kiana Khajeheian, Masoud Haghani, Tahereh Mahmoudi, Sadegh Masjoodi, Masoud Negahdary, Naghmeh Sattarahmady","doi":"10.31661/jbpe.v0i0.2502-1887","DOIUrl":"10.31661/jbpe.v0i0.2502-1887","url":null,"abstract":"<p><strong>Background: </strong>Lung cancer is a leading cause of cancer-related mortality worldwide, underscoring the need for the development of more effective treatment strategies. Radiotherapy (RT), particularly intensity-modulated radiation therapy (IMRT), has enhanced tumor targeting while minimizing damage to healthy tissues. Nevertheless, radioresistance and challenges posed by the tumor microenvironment limit its efficacy.</p><p><strong>Objective: </strong>Selenium-curcumin-polyethylene glycol 600 nanoparticles (Se-Cur-PEG NPs) analyzed as radiosensitizers in IMRT for lung cancer treatment.</p><p><strong>Material and methods: </strong>In this experimental study, Se-Cur-PEG NPs were synthesized and characterized for their potential as radiosensitizers.</p><p><strong>Results: </strong>The <i>in vitro</i> toxicity of Se-Cur-PEG NPs against A549 lung cancer cells was evaluated using MTT assays, demonstrating a dose-dependent reduction in cell viability. The combination of Se-Cur-PEG NPs (50 µg mL^-1) with IMRT (4 Gy) resulted in a significant enhancement in cell death compared to either treatment alone, indicating a strong synergistic effect (CI=1.21) and a notable sensitizer enhancement ratio (SER=2.5). Intracellular ROS generation analysis confirmed that Se-Cur-PEG NPs amplified IMRT-induced oxidative stress, contributing to increased cancer cell toxicity.</p><p><strong>Conclusion: </strong>These findings suggest that Se-Cur-PEG NPs hold promise as effective radiosensitizers, potentially improving lung cancer RT outcomes.</p>","PeriodicalId":38035,"journal":{"name":"Journal of Biomedical Physics and Engineering","volume":"16 1","pages":"11-20"},"PeriodicalIF":0.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12883924/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146158746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of Variability of Dosiomics Features with Varying Prescribed Dose in Prostate Cancer.","authors":"Marziyeh Mirzaeiyan, Simin Hemati, Ali Akhavan, Mahnaz Etehadtavakol, Zahra Sharifonnasabi, Alireza Amouheidari, Atoosa Adibi, Hossein Khanahmad, Parvaneh Shokrani","doi":"10.31661/jbpe.v0i0.2311-1682","DOIUrl":"10.31661/jbpe.v0i0.2311-1682","url":null,"abstract":"<p><strong>Background: </strong>Dosiomics involves converting 3D dose distribution matrices into quantifiable data for analysis. Evaluating the stability of dosiomics features against different prescribed doses is essential before utilizing them for treatment plan assessment.</p><p><strong>Objective: </strong>The current study aimed to investigate dosiomics features variability resulting from different prescribed doses in 3D conformal radiotherapy treatment plans of prostate cancer patients.</p><p><strong>Material and methods: </strong>This retrospective cross-sectional study is conducted based on data from ten prostate cancer patients, and their dose matrices were analyzed to extract features. The stability of dosiomics features was evaluated using the Coefficient of Variation (CV).</p><p><strong>Results: </strong>For each patient, 372 features were extracted for each of the five selected regions of interest. Features with a CV>0.25 have been considered with higher variability. Among the Gray Level Size Zone Matrix group, the Planning Target Volume (PTV) exhibited the highest CV value. Overall, 71% of the features had a CV<0.1, while 5.9% of those had a CV>0.25. Less than 2% of the features had a CV>0.5, and only less than 1% had a CV>1. Features with a CV>0.25 were as follows 33 features in PTV, 60 features in PTV-All, 63 features in PTV-Lymph Node, 65 features in the rectum, and 54 features in the bladder.</p><p><strong>Conclusion: </strong>The prescribed dose significantly influences the variability of dosiomics features during extraction. Understanding these changes is essential for the optimal application of dosiomics in treatment planning for cancer.</p>","PeriodicalId":38035,"journal":{"name":"Journal of Biomedical Physics and Engineering","volume":"16 1","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12883922/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146158772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hough's Transform-Based IoT Device for Automated Identification and Prediction of Blood Groups.","authors":"Vinoth Asokan, Vignesh Ponnu Swamy, Sharmila Baskaran","doi":"10.31661/jbpe.v0i0.2410-1849","DOIUrl":"10.31661/jbpe.v0i0.2410-1849","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":38035,"journal":{"name":"Journal of Biomedical Physics and Engineering","volume":"16 1","pages":"71-80"},"PeriodicalIF":0.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12884055/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146158821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}