Journal of Medical Engineering and Technology最新文献_第2页

Integrating artificial intelligence and bioimpedance spectroscopy for enhanced pancreatic disease diagnosis. 整合人工智能和生物阻抗谱增强胰腺疾病诊断。

Journal of Medical Engineering and Technology Pub Date : 2026-08-18 DOI: 10.1080/03091902.2026.2714536

Sergey Filist, Riad Taha Al-Kasasbeh, Tigran Gevorkyan, Olga Shatalova, Nikolay A Korenevskiy, Osama M Al-Habahbeh, Nadin Munther Habash, Ahmad Telfah, Mohammad Al-Jundi, Ashraf Shaqadan

{"title":"Integrating artificial intelligence and bioimpedance spectroscopy for enhanced pancreatic disease diagnosis.","authors":"Sergey Filist, Riad Taha Al-Kasasbeh, Tigran Gevorkyan, Olga Shatalova, Nikolay A Korenevskiy, Osama M Al-Habahbeh, Nadin Munther Habash, Ahmad Telfah, Mohammad Al-Jundi, Ashraf Shaqadan","doi":"10.1080/03091902.2026.2714536","DOIUrl":"https://doi.org/10.1080/03091902.2026.2714536","url":null,"abstract":"The purpose of this study is to develop bioimpedance spectroscopy methods for diagnosing pancreatic diseases. We developed a descriptor approach using impedance spectroscopy results, generating four amplitude-phase-frequency responses from four quasi-orthogonal leads. They create the feature spaces required for our hybrid classifier for diagnosing pancreatic diseases, consisting of five macro levels, the first of which is based on probabilistic neural networks, and the remaining four are based on fuzzy inference. We also presented a device structure for generating informative feature spaces. The use of multi-frequency sensing in classifiers based on fully connected neural networks made it possible to develop a clinical decision support system for diagnosing pancreatic diseases. The results were confirmed in groups of patients aged from 25 to 80 years, male and female, at different stages of pancreatic disease. The training and testing samples were formed using various diagnostic methods, including medical history, physical examination, assessment of comorbidities, laboratory tests, ultrasound, laparoscopy, intraoperative revision, and computed tomography. An assessment of the diagnostic quality indicators of a hybrid neural network for three classes of diseases: \"pancreatic cancer,\" \"chronic pancreatitis\" and \"without pancreatic pathology\" showed that their maximum value for differentiating these classes of diseases was 89%, the minimum -63%. Experimental software studies and clinical trials of our decision support system demonstrated diagnostic sensitivity and specificity comparable to existing methods, validating the practicality of our methods. This research opens new possibilities for accessible and objective diagnosis of pancreatic diseases, expanding the ability to make intelligent medical decisions.","PeriodicalId":39637,"journal":{"name":"Journal of Medical Engineering and Technology","volume":" ","pages":"1-13"},"PeriodicalIF":0.0,"publicationDate":"2026-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148799175","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}

引用次数: 0

High precision segmentation of glioma and surroundings - a feasibility study using multiparametric MRI and deep learning. 神经胶质瘤和周围环境的高精度分割-使用多参数MRI和深度学习的可行性研究。

Journal of Medical Engineering and Technology Pub Date : 2026-08-14 DOI: 10.1080/03091902.2026.2713703

Arvid Lundervold, Saruar Alam, Marianne H Hannisdal, Dorota Goplen, Martha Chekenya, Alexander S Lundervold

{"title":"High precision segmentation of glioma and surroundings - a feasibility study using multiparametric MRI and deep learning.","authors":"Arvid Lundervold, Saruar Alam, Marianne H Hannisdal, Dorota Goplen, Martha Chekenya, Alexander S Lundervold","doi":"10.1080/03091902.2026.2713703","DOIUrl":"https://doi.org/10.1080/03091902.2026.2713703","url":null,"abstract":"Glioblastoma (GBM) is an aggressive primary brain cancer in which precise spatial characterisation of tumour sub-compartments and surrounding anatomy may support research into targeted treatment. We designed and assessed a feasibility pipeline for subject-specific localisation of glioma and brain regions from standard multiparametric MRI (T1, T1-Gadolinium, T2, FLAIR), combining deep-learning segmentation with robust anatomical parcellation rather than atlas coregistration. Coregistered images yield three tumour compartments: central non-enhancing/necrotic tumour, enhancing tumour, and surrounding edoema. From this multichannel representation, we derive tumour volumes and regional tumour burden \"hit-plots\" that show, at the voxel level, which brain regions each compartment intersects. In a cohort of <math><mrow><mi>n</mi><mo>=</mo><mn>50</mn></mrow></math> UCSF-PDGM glioblastoma subjects, deep-learning segmentations agreed closely with the reference masks: median Dice of 0.90 (whole tumour), 0.94 (tumour core), and 0.86 (enhancing tumour), with corresponding HD95 values of 4.1, 2.2, and 2.0 mm. In one longitudinal LUMIERE subject (Patient-048, six timepoints), the pipeline recovered concordant volumetric trajectories and evolving regional-burden patterns with respect to specific brain structures, in agreement with independent comparator segmentations. This subject-specific regional tumour profile may in the future serve as a descriptive anatomical context for radiotherapy target delineation.","PeriodicalId":39637,"journal":{"name":"Journal of Medical Engineering and Technology","volume":" ","pages":"1-36"},"PeriodicalIF":0.0,"publicationDate":"2026-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148762570","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}

引用次数: 0

Development of a digital dual-channel pressure biofeedback unit: a proof-of-concept for clinically driven re-engineering. 数字双通道压力生物反馈装置的开发：临床驱动的重新设计的概念验证。

Journal of Medical Engineering and Technology Pub Date : 2026-08-08 DOI: 10.1080/03091902.2026.2713029

Rafet Irmak

{"title":"Development of a digital dual-channel pressure biofeedback unit: a proof-of-concept for clinically driven re-engineering.","authors":"Rafet Irmak","doi":"10.1080/03091902.2026.2713029","DOIUrl":"https://doi.org/10.1080/03091902.2026.2713029","url":null,"abstract":"The monitoring of lumbopelvic therapeutic exercise has traditionally relied on commercial pressure biofeedback units limited to single-channel analogue systems. Consequently, the translation of an integrated approach capable of simultaneously assessing transvaginal and external abdominal pressure dynamics into routine clinical practice has remained limited. To address this limitation, the present study introduces a proof-of-concept digital architecture designed to integrate two distinct pressure-monitoring interfaces within a synchronised, low-cost platform. Utilising a PIC16F1789 microcontroller and dual differential pressure sensors, the system features continuous wireless telemetry and a transparent R-based interface. A comprehensive comparator-based static bench characterisation (20-100 mmHg) demonstrated a high degree of linearity (R2 > 0.99), with a mean absolute error of less than 1.1 mmHg and an inter-channel standard deviation of 0.72 mmHg. The architecture yielded a reliable dual-channel effective frame rate of approximately 39 Hz. While this prototype currently functions as a robust bench-level demonstrator rather than a clinically validated diagnostic tool, it provides an open-hardware blueprint for the engineering community. This foundation may support future investigation of time-aligned pressure monitoring and its potential relevance to lumbopelvic rehabilitation.","PeriodicalId":39637,"journal":{"name":"Journal of Medical Engineering and Technology","volume":" ","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2026-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148697866","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}

引用次数: 0

Enhancing cardiovascular disease diagnosis through knowledge distillation and hybrid Transformer-CNN-SENet architecture. 基于知识升华和混合Transformer-CNN-SENet架构增强心血管疾病诊断。

Journal of Medical Engineering and Technology Pub Date : 2026-08-07 DOI: 10.1080/03091902.2026.2713702

Nasim Beigzadeh, Abdolhossein Fathi

{"title":"Enhancing cardiovascular disease diagnosis through knowledge distillation and hybrid Transformer-CNN-SENet architecture.","authors":"Nasim Beigzadeh, Abdolhossein Fathi","doi":"10.1080/03091902.2026.2713702","DOIUrl":"https://doi.org/10.1080/03091902.2026.2713702","url":null,"abstract":"Worldwide, cardiovascular diseases (CVDs) are the leading cause of mortality, making early detection a critical public health objective. The analysis of electrocardiograms (ECGs) is a fundamental tool for CVD diagnosis, and recent developments in deep learning have greatly enhanced its diagnostic accuracy. A 12-lead ECG dataset provides valuable information for enhancing diagnostic performance, but extracting and revealing these patterns requires complex and hidden models with numerous parameters and high computational demands. In portable and wearable healthcare devices, GPUs with low power are not suitable for deploying these models. The purpose of this study is to address these limitations by presenting a knowledge distillation (KD) framework that transfers knowledge from a high-capacity teacher model (T-CST) to a lightweight student model (S-CS). A forward lead selection strategy is introduced to identify the most informative single lead or optimal small-lead combinations for efficient diagnosis with minimal data input. The teacher model integrates CNN, SENet, and Transformer modules to capture both local and long-range temporal dependencies, enabling it to extract a diverse set of hidden and comprehensive patterns from 12-lead ECG signals, including subtle temporal relationships and intricate local features. The compact student model, consisting of a CNN and SENet, leverages this transferred knowledge to achieve comparable performance with substantially lower computational cost. Experiments on the PTB-XL dataset demonstrate that S-CS attains near-teacher accuracy using only three leads while reducing computation by 63 times, highlighting its promise for efficient, accurate, and accessible CVD detection in portable and wearable healthcare systems.","PeriodicalId":39637,"journal":{"name":"Journal of Medical Engineering and Technology","volume":" ","pages":"1-22"},"PeriodicalIF":0.0,"publicationDate":"2026-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148686178","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}

引用次数: 0

Initial simplified design on cerebrovascular for haemodynamic effects: correlation analysis between stenosis severity and disruption in velocity, pressure, and wall shear stress. 脑血管血流动力学效应的初步简化设计：狭窄严重程度与速度、压力和壁剪应力破坏的相关性分析。

Journal of Medical Engineering and Technology Pub Date : 2026-08-05 DOI: 10.1080/03091902.2026.2710649

Nurul Syazana Qhairunessa Salleh, Nabilah Ibrahim, Nur'Afifah Yousri, Iskhrizat Taib, Shahnoor Shanta

{"title":"Initial simplified design on cerebrovascular for haemodynamic effects: correlation analysis between stenosis severity and disruption in velocity, pressure, and wall shear stress.","authors":"Nurul Syazana Qhairunessa Salleh, Nabilah Ibrahim, Nur'Afifah Yousri, Iskhrizat Taib, Shahnoor Shanta","doi":"10.1080/03091902.2026.2710649","DOIUrl":"https://doi.org/10.1080/03091902.2026.2710649","url":null,"abstract":"This study investigated the haemodynamic effects by simulating simplified cerebrovascular at different stenosis levels. To improve the understanding of haemodynamic changes due to atherosclerosis, computational fluid dynamics (CFD) simulations are employed to visualise wall shear stress (WSS), pressure distribution, and flow rate within arterial models. Three different degrees of arterial atherosclerosis, specifically 25%, 50% and 75% plaque thickness have been examined. Findings show that in the severe atherosclerosis of 75%, blood flow velocity through the plaque undergoes a significant change, leading to low pressure due to extreme narrowing and resulting turbulent flow. Moreover, WSS contours reveal extensive and severe red zones when atherosclerosis reaches 75%, especially within and beyond the severely constricted region. Although the individual parameters were visualised, the velocity-pressure correlation for each atherosclerosis level showed velocity peaks around 10 m/s for 50% plaque and sharply increasing to nearly 100 m/s for 75% plaque. In mild atherosclerosis (25% plaque), both velocity and pressure profiles remain relatively stable. This study offers deeper insights and understanding on how atherosclerosis alters cerebral circulation, potentially enhancing diagnostic accuracy and treatment strategies for stroke prevention.","PeriodicalId":39637,"journal":{"name":"Journal of Medical Engineering and Technology","volume":" ","pages":"1-13"},"PeriodicalIF":0.0,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148680408","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}

引用次数: 0

Reconsidering generalizability and clinical reliability in ERP-based ADHD classification models. 重新考虑基于erp的ADHD分类模型的通用性和临床可靠性。

Journal of Medical Engineering and Technology Pub Date : 2026-07-27 DOI: 10.1080/03091902.2026.2706791

Muhammad Ibnu Sa'ad, Sjamsir Hasbi, Ayu Aprilia Pangestu Putri, Andi Wahyu Irawan

引用次数: 0

Fabrication and investigation of chitosan/PVA and chitosan/PVA/nisin nanofibers via electrospinning as potential wound dressing agents. 静电纺丝法制备壳聚糖/聚乙烯醇和壳聚糖/聚乙烯醇/乳酸蛋白纳米纤维作为潜在的伤口敷料。

Journal of Medical Engineering and Technology Pub Date : 2026-07-17 DOI: 10.1080/03091902.2026.2688780

Sepideh Ebrahimpoor, Azadeh Asefnejad, Mohsen Mohammadi

{"title":"Fabrication and investigation of chitosan/PVA and chitosan/PVA/nisin nanofibers via electrospinning as potential wound dressing agents.","authors":"Sepideh Ebrahimpoor, Azadeh Asefnejad, Mohsen Mohammadi","doi":"10.1080/03091902.2026.2688780","DOIUrl":"https://doi.org/10.1080/03091902.2026.2688780","url":null,"abstract":"The objective of this study was to fabricate and evaluate chitosan(CS)/polyvinyl alcohol(PVA) nanofibrous wound dressings incorporated with nisin and compare their performance with CS-PVA scaffolds. Nanofibers were produced via electrospinning under optimised conditions (18 kV voltage, 0.2 mL/h flow rate, and 120 mm tip-to-collector distance). Scanning electron microscopy revealed that incorporation of nisin increased the average fibre diameter from 114.5 nm to 125.8 nm, while porosity decreased from 89.13% to 86.21%. Antibacterial activity was evaluated against Staphylococcus aureus and Escherichia coli. CS-PVA scaffolds showed no inhibition, whereas CS-PVA-nisin exhibited significant antibacterial activity with inhibition zones of 34.5 mm for S. aureus and 25.4 mm for E. coli. FTIR confirmed the successful incorporation of nisin through characteristic peaks at 3330 cm-1 and 2918 cm-1. Addition of nisin did not significantly affect tensile strength (1.6 MPa for both samples), although tensile strain decreased from 8% to 5.1% and Young's modulus increased from 20 MPa to 31.37 MPa. Contact angle measurements increased from 49° to 59°, indicating reduced hydrophilicity. MTT assay results demonstrated high cell viability for both scaffolds (95.2% for CS-PVA and 94.7% for CS-PVA-nisin), confirming their non-cytotoxic nature Overall, CS-PVA-nisin nanofibers exhibited enhanced antibacterial performance while maintaining suitable mechanical properties and biocompatibility.","PeriodicalId":39637,"journal":{"name":"Journal of Medical Engineering and Technology","volume":" ","pages":"1-13"},"PeriodicalIF":0.0,"publicationDate":"2026-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148473161","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}

引用次数: 0

Chronic renal disease classification using the AL-5-ENeT-B4 model from CT images. 利用CT图像中的AL-5-ENeT-B4模型进行慢性肾脏疾病分类。

Journal of Medical Engineering and Technology Pub Date : 2026-07-14 DOI: 10.1080/03091902.2026.2701813

Ram Kishun Mahto, Pushpendra Kumar, Subhash Chandra Yadav

{"title":"Chronic renal disease classification using the AL-5-ENeT-B4 model from CT images.","authors":"Ram Kishun Mahto, Pushpendra Kumar, Subhash Chandra Yadav","doi":"10.1080/03091902.2026.2701813","DOIUrl":"https://doi.org/10.1080/03091902.2026.2701813","url":null,"abstract":"Chronic Renal Disease (CRD) is an increasing global health burden, largely driven by the rising prevalence of diabetes and hypertension. Early and accurate identification of renal abnormalities from computed tomography (CT) images is clinically important, yet manual interpretation is time-consuming and conventional machine learning methods often have limited scalability for complex multiclass medical image classification. This study proposes AL-5-ENeT-B4, a modified EfficientNet-B4 architecture enhanc d with five additional layers for automated CRD classification from CT images. The proposed framework is integrated an image preprocessing, data augmentation, stratified 5-fold cross-validation, and deep learning of pre-trained features to classify renal CT scans into one of the four categories: cyst, normal, stone, or tumour. The results of this model were evaluated using 5-fold cross-validation. The proposed model achieved an average K-fold accuracy (99.18%), precision (99.18%), and recall score (99.19%), with an F1-score (99.18%). Furthermore, Grad-CAM visualisation used to provide better interpretability by highlights those renal areas that had clinical significance in relation to the model's decisions. The proposed AL-5-ENeT-B4 model is compared with ResNet50, VGG16, DenseNet121, and ViT-B/16 and demonstrated significantly improved performance achieving an average MCC of 0.9912 and Cohen's Kappa of 0.9912(p < 0.001).","PeriodicalId":39637,"journal":{"name":"Journal of Medical Engineering and Technology","volume":" ","pages":"1-16"},"PeriodicalIF":0.0,"publicationDate":"2026-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148449533","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}

引用次数: 0

Microwave-assisted thermal profiling of blood: a potential biomarker for differentiating cancer and non-cancer states. 微波辅助血液热谱分析：鉴别癌症和非癌症状态的潜在生物标志物。

Journal of Medical Engineering and Technology Pub Date : 2026-07-09 DOI: 10.1080/03091902.2026.2698512

Subhasish Sarkar, Ritabrata Saha, Avishek Dutta, Rik Chattopadhyay, Dibyendu Maity, Indranil Biswas, Arup Mondal, Abhik Ghosh, Ranjan Ganguly, Apurba K Santra, Utpal Garain, Debasis Mitra, Suman Chakrabarty

{"title":"Microwave-assisted thermal profiling of blood: a potential biomarker for differentiating cancer and non-cancer states.","authors":"Subhasish Sarkar, Ritabrata Saha, Avishek Dutta, Rik Chattopadhyay, Dibyendu Maity, Indranil Biswas, Arup Mondal, Abhik Ghosh, Ranjan Ganguly, Apurba K Santra, Utpal Garain, Debasis Mitra, Suman Chakrabarty","doi":"10.1080/03091902.2026.2698512","DOIUrl":"https://doi.org/10.1080/03091902.2026.2698512","url":null,"abstract":"Reliable differentiation between cancer and non-cancer states using minimally invasive approaches remains a significant challenge. Most currently available biomarkers are cancer-type specific and may not capture systemic alterations associated with malignancy. This study evaluates the feasibility of microwave-assisted thermal profiling of blood as a method to distinguish cancer from non-cancer conditions. A cross-sectional analytical study was conducted involving 232 participants (87 cancer patients and 145 non-cancer individuals). A custom-built device was used to perform microwave heating of 0.5 mL venous blood samples. Temperature rise patterns were recorded using an infra-red thermal imager. Key parameters, including the time taken to reach the maximum temperature and maximum gradient time, temperature increase and rate of temperature increase, were extracted and statistically analysed. Significant differences in thermal profiles between cancer and non-cancer groups were observed. Cancer patients exhibited a higher Gradient time ratio (GTR) and lower Zblood value, which indicates an alteration in thermal profile of blood due to cancer. The non-probabilistic prediction rule achieved 96.10 accuracy, with a sensitivity of 96.54 and specificity of 95.86. The microwave-based thermal profiling method demonstrated high diagnostic accuracy and has the potential to serve as a reliable cancer biomarker. Further validation in larger cohorts is required.","PeriodicalId":39637,"journal":{"name":"Journal of Medical Engineering and Technology","volume":" ","pages":"1-16"},"PeriodicalIF":0.0,"publicationDate":"2026-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148413056","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}

引用次数: 0

PMMA based ultra miniaturized implantable antenna for biotelemetry applications. 基于PMMA的生物遥测应用的超小型植入式天线。

Journal of Medical Engineering and Technology Pub Date : 2026-07-01 Epub Date: 2026-06-03 DOI: 10.1080/03091902.2026.2677020

Sakshi Kumari, Sheelu Kumari, Vibha Rani Gupta

{"title":"PMMA based ultra miniaturized implantable antenna for biotelemetry applications.","authors":"Sakshi Kumari, Sheelu Kumari, Vibha Rani Gupta","doi":"10.1080/03091902.2026.2677020","DOIUrl":"10.1080/03091902.2026.2677020","url":null,"abstract":"Recent advancement in engineered biomedical materials has facilitated breakthroughs in the field of the medical world. Poly (Methyl Methacrylate) PMMA is explored as a promising biocompatible substrate for an implantable antenna, whose dielectric properties are experimentally characterised before antenna design, ensuring realistic performance evaluation in biological tissues. This paper proposes a PMMA-based compact miniaturised implantable antenna of size 38.4 mm3 for the wireless medical telemetry service (WMTS) band operating at 1.4 GHz. The proposed antenna can be implanted either in the skin or the muscle. The gain of the antenna is -17.93 dBi and -28.15 dBi in the skin and muscle tissue, respectively, with a specific absorption rate (SAR) of less than 1.6 Watts/kg. The performance characteristics of the fabricated prototypes are verified by implanting them in human tissues, emulated phantom models of skin and muscle, which are observed to be in line with the simulated results. Finally, a robust link budget is established for biotelemetry applications in the indoor short-range communication system, based on certain constraints (power limitation, SAR, and frequency).","PeriodicalId":39637,"journal":{"name":"Journal of Medical Engineering and Technology","volume":" ","pages":"484-494"},"PeriodicalIF":0.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148158530","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}

引用次数: 0