Biomedical Physics & Engineering Express最新文献

{"title":"Remote patient monitoring system combining hardware and artificial intelligence based software.","authors":"Kaan Kıvırcık, Sibel Çimen, Nilay Bulduk, Orhan Er, Mehmet Sagbas","doi":"10.1088/2057-1976/ae0f1f","DOIUrl":"10.1088/2057-1976/ae0f1f","url":null,"abstract":"<p><p>This study details the development of a remote patient monitoring system with a primary focus on a novel, customized Deep Neural Network (DNN) for arrhythmia detection. The system integrates hardware for real-time data collection from biomedical sensors, where IoT-based sensor data is collected and encrypted in a central database for subsequent analysis. The novelty of the work lies in the proposed AI-based software component rather than the hardware assembly, which utilizes accessible components. The developed system is designed to function as a decision support system for healthcare personnel, providing necessary information and alerts through mobile and desktop interfaces. Data obtained from the patient is classified using the proposed deep learning method, and a detailed summary is presented. The customized DNN-based model demonstrated a test accuracy of 99.94%, with a recall of 99.92% and a precision of 99.57%, results which indicate a strong potential for clinical application due to very low false positive and false negative rates. Based on this high accuracy, the model's outputs have been integrated into user-friendly interfaces to assist healthcare personnel. It is therefore suggested that the patient monitoring system, featuring this high-performance classification model, has the potential to contribute to the early and more reliable detection of significant diseases such as heart abnormalities and arrhythmia.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145224863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SCIMITAR: optimising chest digital tomosynthesis devices using geometric simulations and genetic algorithms.","authors":"Alexander D Hill, Daliya Aflyatunova, Aquila Mavalankar, Stephen Wells, D Keith Bowen, Fraser Holloway, Lauryn Eley, Ishbel Jamieson, Matteo Contino, Carsten P Welsch","doi":"10.1088/2057-1976/ae0fa0","DOIUrl":"10.1088/2057-1976/ae0fa0","url":null,"abstract":"<p><p><i>Objective</i>. Digital tomosynthesis (DT) bridges the gap between planar x-rays and computed tomography, offering rapid, low-dose 3D imaging. A mobile chest DT device could transform procedures such as nasogastric tube placement and early cancer detection. Adaptix Ltd. has developed 3D imaging systems using cold-cathode x-ray emitter arrays on flat panel source (FPS) units for veterinary and orthopaedic applications. Designing a chest DT device using multiple FPSs presents new challenges, requiring simulations that can efficiently explore the large design space and rapidly identify optimal configurations.<i>Approach</i>. We developed Scimitar, a geometry-based simulation framework that models x-ray radiation coverage in chest DT systems. It evaluates design viability and performance using irradiation uniformity metrics and integrates a genetic algorithm to optimise key system parameters. Scimitarfurther facilitates the evaluation of collimator designs, FPS arrangements, engineering constraints, and dynamic adaptation to different patient volumes.<i>Main results</i>. Square collimators generally outperformed circular designs due to better alignment with the cuboid target volume. Across FPS configurations, optimisation consistently yielded maximum source-to-image distances, minimal emitter spacing, and x-ray cone angles near 30°. A four-panel cross arrangement achieved highest uniformity. Imposing engineering constraints such as increased emitter spacing led to approximately linear reductions in uniformity. Introducing vertical offsets to central panels yielded modest gains, though still underperformed compared to configurations without central panels. Dynamic cone angle adjustment enabled device adaptation to different patient sizes, with the four-panel cross consistently delivering the best results.<i>Significance</i>. Scimitarefficiently optimises chest DT designs under various constraints and assumptions. This work identifies promising configurations, highlights design trade-offs, and demonstrates adaptability across patient sizes. As understanding of system requirements evolve, Scimitar's adaptability will enable it to remain a valuable tool in guiding the development of clinically effective, low-dose, mobile 3D imaging devices.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145237618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mathematical Modelling of Magnetic Field and Nanoparticle Effects on Calcium Signalling in Malignant Esophageal Cells.","authors":"Yevhen Salatskyi, Svitlana Vasylivna Gorobets, Oksana Yuriivna Gorobets","doi":"10.1088/2057-1976/ae1140","DOIUrl":"https://doi.org/10.1088/2057-1976/ae1140","url":null,"abstract":"<p><strong>Purpose: </strong>This study aimed to develop a mathematical model investigating how low-frequency magnetic fields and magnetic nanoparticles theoretically affect calcium signalling in esophageal squamous cell carcinoma (ESCC) cells through mechanosensitive channel activation.</p><p><strong>Methods: </strong>We modified the Chang model to incorporate magnetic field-induced membrane shear stress mechanisms, simulating intracellular calcium dynamics using ordinary differential equations in Python. The model examined rotating magnetic fields at 25 mT across frequencies from 0 to 1.7π mHz, analyzing calcium oscillation patterns and their potential effects on mitogen-activated protein kinase (MAPK) signalling pathways.</p><p><strong>Results: </strong>Simulations demonstrated that low-frequency rotating magnetic fields at 1.7π mHz and lower frequencies disrupted normal calcium oscillations, creating inter-burst periods of at least 588.2 seconds. This minimum period exceeds the sensitivity threshold of MAPK signalling (1.7-17 mHz), suggesting potential inhibition of proliferation pathways dependent on calcium oscillation frequency. The model predicted reduced oscillation magnitude and altered temporal dynamics compared to control conditions.</p><p><strong>Conclusions: </strong>The mathematical framework provides theoretical foundation for magnetic field interactions with cellular calcium dynamics through mechanosensitive channels, offering conceptual basis for potential therapeutic applications. All findings require comprehensive experimental validation before any clinical implications can be considered.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145257270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimized hybrid RNN-GRU model for predictive diagnosis of cardiovascular disease.","authors":"Gaurav Kumar, Neeraj Varshney","doi":"10.1088/2057-1976/ae0d95","DOIUrl":"10.1088/2057-1976/ae0d95","url":null,"abstract":"<p><p>Cardiovascular disease (CVD) continues to be the leading cause of death for individuals all over the globe, and India bears a disproportionate share of the burden associated with this condition. A hybrid deep learning model that combines Recurrent Neural Networks (RNN) and Gated Recurrent Units (GRU) is being used in this research project with the objective of enhancing the accuracy and efficiency of heart disease risk prediction. It makes use of a dataset consisting of 918 samples that was obtained from IEEE Dataport. It then applies preprocessing processes such as the correction of outliers using the Interquartile Range (IQR) technique and the normalization of numerical characteristics. The use of Synthetic Minority Over Sampling Technique (SMOTE) to get a balanced dataset, the dataset is then divided into training and testing sets. For the purpose of fine-tuning the model, GridSearchCV was used in conjunction with 10-fold cross-validation. The results demonstrated that the hybrid RNN-GRU model greatly outperformed the performance of the separate RNN and GRU models. It achieved an accuracy of 99.6%, a 99.6% F1 score, a 99.6% precision, and a 99% recall, which was higher than the highest reported model accuracies of 87% and 97%. The results of this study demonstrated that the capacity of RNNs to process sequences, when paired with the gating properties of GRUs, allows the extraction of temporal parameters from cardiac signals. The significance of appropriate data processing highlights the potential contribution of the model to clinical decision-making procedures that are targeted at early and more accurate detection of cardiac disease.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145197918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of shared dense connectivity and channel width on convolutional block attention module for regional MRI-based brain tumor classification.","authors":"Binish M C, Vinu Thomas","doi":"10.1088/2057-1976/ae062b","DOIUrl":"https://doi.org/10.1088/2057-1976/ae062b","url":null,"abstract":"<p><p>MR imaging is a widely used imaging technique for diagnosing brain-related issues. Different tumor types in MR images often share similar visual characteristics, leading to misclassification. This research focused on the impact of multiple shared dense channel attention (MSDCAT) with varying dimensionality on the CBAM(convolutional block attention module) architecture, which is further used for the classification of MR Images for detecting brain tumors. The major objectives addressed in this study are to enhance feature extraction capabilities through multiple shared dense layers, the effect of channel reduction ratio, and to enable efficient information flow across multiple layers. The proposed model leverages the dense connectivity and feature reuse properties of Dense block to extract discriminative features from multi-modal MRI images. The model includes 4 shared dense layers on the channel attention module in conjunction with the spatial attention module in a sequential manner. The structured dense block with a transition layer is also included in the initial pathways. The model is evaluated on varying scenarios of shared dense layers and multiple channel reduction ratios on different standardized databases. Testing of the proposed system on the Figshare database, together with the Kaggle database, demonstrated promising outcomes and produced strong accuracy rates with specific sensitivity and specificity measurements. The model reached 99.70% accuracy in the Figshare database while achieving 99.90% accuracy in the Kaggle dataset. Our findings demonstrated the feature extraction capability of the proposed approach in accurately classifying brain tumors on both datasets and highlighting the potential of Multi-Layered shared dense layers in accurately extracting the channel attention features from the MRI(Magnetic Resonance Imaging) images.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":"11 6","pages":""},"PeriodicalIF":1.6,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145249519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SDM-YOLO11n: A Lightweight and High-Precision Infusion Monitoring Method.","authors":"Xiangyu Deng, Wenbo Dong, Zhecong Fan","doi":"10.1088/2057-1976/ae103b","DOIUrl":"https://doi.org/10.1088/2057-1976/ae103b","url":null,"abstract":"<p><p>Current infusion monitoring methods primarily rely on two technological approaches: nonvisual sensor technology and visual sensor technology, for real-time monitoring of the remaining liquid volume in infusion bottles within infusion scenarios. However, non-visual sensor-based methods often suffer from complex installation procedures and are prone to external interference, while visual sensor-based methods tend to exhibit low detection accuracy in complex infusion environments involving small targets, low contrast, tilted objects, and partial occlusions, making it difficult to accurately monitor the remaining liquid. To address these challenges, we propose a high-precision and lightweight object detection algorithm-SDM-YOLO11n-based on an improved version of YOLO11n. Specifically, a lightweight spatial perception convolution module (SPConv) is introduced to enhance the backbone network's spatial modeling capabilities and improve feature extraction efficiency; the traditional upsampling operation is replaced with a dynamic sampling module (DySample) for more adaptive feature reconstruction and multi-scale information fusion; and a mixed local channel attention mechanism (MLCA) is incorporated to strengthen attention to key regions of infusion bottles and their internal liquids, thereby further improving detection accuracy. In addition, a method based on the ratio of geometric parameters of oriented bounding boxes is proposed to precisely estimate the remaining liquid volume in infusion bottles. Experimental results show that SDM-YOLO11n improves mAP@0.5:0.95 by 0.6 percentage points compared to YOLO11n, with a model size of only 5.1 MB. The proposed algorithm achieves high-precision detection of infusion bottles and their internal liquids in complex scenarios and enables real-time monitoring of the remaining liquid volume in multiple infusion bottles.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145243658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"In-Vivo Reflection Terahertz Imaging for Non-Invasive Skin Diagnostics: A Topical Review.","authors":"Naveen Sharma, Swetha Duvuri, Ashu Rastogi, Sarbhjeet Singh Singh, Neerja Garg, Virendra Kumar","doi":"10.1088/2057-1976/ae1038","DOIUrl":"https://doi.org/10.1088/2057-1976/ae1038","url":null,"abstract":"<p><p>Medical imaging has revolutionized disease detection and patient care; however, conventional modalities such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT), ultrasound, and optical imaging have inherent limitations in sensitivity, penetration depth, and safety. Terahertz (THz) imaging is an emerging, non-ionizing technique that offers high sensitivity to water content and molecular composition, making it particularly suitable for skin diagnostics. This review provides a comparative analysis of transmission and reflection-mode THz imaging, with a detailed focus on the two primary reflection techniques-Terahertz Pulsed Imaging (THz-PI) and Continuous-Wave Terahertz Imaging (CW-THz). Their working principles, benefits, limitations, and clinical relevance are critically evaluated. Reflection-mode THz imaging shows strong potential for biological tissue analysis, offering high contrast for detecting skin malignancies, assessing hydration levels, monitoring wound healing, and evaluating transdermal drug delivery. Despite ongoing challenges in penetration depth and real-time imaging, advancements in AI-based analysis, multimodal integration, and system miniaturization are progressively enhancing its clinical applicability. This review serves as a comprehensive resource for researchers and clinicians aiming to integrate THz imaging into skin diagnostics. It highlights the transformative potential of THz technology in facilitating early disease detection, enabling personalized treatment strategies, and advancing the future of biomedical imaging.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145243686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cardiovascular Risk Prediction in Diabetes: A Hybrid Machine Learning Approach.","authors":"Imran Rehan, Mujeeb Ur Rehman","doi":"10.1088/2057-1976/ae103a","DOIUrl":"https://doi.org/10.1088/2057-1976/ae103a","url":null,"abstract":"<p><p>Cardiovascular disease (CVD) is a major cause of morbidity and mortality in diabetic populations. Early detection of cardiovascular risk in diabetes is crucial to reduce complications, particularly in resource-limited settings. This study aimed to develop and evaluate a hybrid machine learning framework that integrates Long Short-Term Memory (LSTM) networks with traditional algorithms to improve cardiovascular risk prediction in diabetic patients. The hybrid model, which included structured data and time-series health data, was tested on a sample of 1,000 diabetes patients. Using 10-fold cross-validation, the model achieved impressive predictive performance (accuracy 98.7%, AUC 0.99). There are three main conclusions from this study. Initially, the hybrid model demonstrated a significant increase in CVD prediction accuracy when compared to independent machine-learning techniques. Second, the model provided reasonable predictions across different demographic groupings, ensuring equitable outcomes. Finally, the model's high performance supports its potential for future use in clinical decision-support systems aimed at improving outcomes and optimizing resource allocation. Increased CVD screening rates in diabetic patients, better access to care for communities with limited resources, and the advancement of health equity are all possible outcomes of incorporating machine learning and deep learning techniques. The proposed hybrid model also demonstrates strong potential for clinical deployment in cardiovascular risk prediction among diabetic populations, supporting earlier interventions and improved patient outcomes.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145243683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optical scattering coefficient measurement of blood plasma during clot formation.","authors":"Lea Abi Nassif, Wadih Khater, Fabrice Pellen, Bernard Le Jeune, Marie Abboud, Marc Danguy des Déserts, Benjamin Espinasse, Guy Le Brun","doi":"10.1088/2057-1976/ae103c","DOIUrl":"https://doi.org/10.1088/2057-1976/ae103c","url":null,"abstract":"<p><strong>Objective: </strong>&#xD;Venous Thromboembolism (VTE) is a very dangerous and common disease. While approximately 50% of VTE can be attributed to identifiable causes, the remaining half has no known origin and about 30% of this group show recurrence. Monitoring the kinetics of optical scattering properties of plasma during clot formation can provide information on clot structure, which seems to be a relevant parameter to identify patients at risk of recurrence.&#xD;Approach:&#xD;This study aims to compare a sensitive and quantitative optical method based on the scattering coefficient µs measurement, never previously explored in the context of VTE, to the conventional optical density (OD) measurement obtained by a spectrophotometer.&#xD;Main results:&#xD;The evolution of eight characteristic parameters, extracted from clot formation curve signatures, was studied as a function of plasma concentration.&#xD;Significance:&#xD;Scattering coefficient measurements are sensitive to plasma concentration in the sample, more reproductible, more precise and provide access to new information compared to OD measurements.&#xD.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145243676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ECG Beat Classification with Fractional Order Differentiator and Machine Learning Techniques.","authors":"H K Prasad Katamreddi, Tirumala Krishna Battula","doi":"10.1088/2057-1976/ae103d","DOIUrl":"https://doi.org/10.1088/2057-1976/ae103d","url":null,"abstract":"<p><p>Electrocardiogram (ECG) is essential for assessing heart function, but manual analysis is time-consuming and error-prone. Automated ECG analysis can improve early detection of cardiovascular diseases by accurately identifying abnormal beats despite signal irregularity and non-stationarity. In this work, a novel approach for accurate ECG beat classification was proposed, integrating a sequential approach with a fractional order differentiator, dual-tree complex wavelet transform (DTCWT) features, and machine learning (ML) classifiers. This methodology involves R-peak detection using a fractional order differentiator, feature extraction with DTCWT, and classification using various ML models. Evaluated on the MIT-BIH Arrhythmia Database, this approach demonstrates superior performance, with the Random Forest classifier achieving an accuracy of 96.82%, sensitivity of 96.83%, specificity of 97.02%, PPV of 96.89%, and an F1 score of 96.85%. These results underscore the effectiveness of this approach in improving the accuracy of ECG beat classification, contributing to better clinical outcomes in heart disease diagnosis.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145243705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}