Journal of Medical Engineering and Technology最新文献_第3页

Indirect calorimetry to study the caloric demand in spine surgery: a technical note. 间接量热法研究脊柱手术的热量需求：技术说明。

Journal of Medical Engineering and Technology Pub Date : 2026-07-01 Epub Date: 2026-05-08 DOI: 10.1080/03091902.2026.2667349

Matteo Briguglio, Marialetizia Latella, Paola Vella, Tiziano Crespi, Paolo Perazzo, Thomas W Wainwright, Riccardo Cecchinato, Francesco Langella, Pedro Berjano

引用次数: 0

Genetic algorithm-optimized machine learning approaches for EEG-based silent speech decoding. 基于脑电图的无声语音解码的遗传算法优化机器学习方法。

Journal of Medical Engineering and Technology Pub Date : 2026-07-01 Epub Date: 2026-05-25 DOI: 10.1080/03091902.2026.2676658

Kamya Hari, Anjali Anand, Afnan Naveed, Vijayalakshmi P

{"title":"Genetic algorithm-optimized machine learning approaches for EEG-based silent speech decoding.","authors":"Kamya Hari, Anjali Anand, Afnan Naveed, Vijayalakshmi P","doi":"10.1080/03091902.2026.2676658","DOIUrl":"10.1080/03091902.2026.2676658","url":null,"abstract":"The phases of human communication consist of speech perception, production, and imagination. The objective of this work is to understand and analyse the changes that occur in the neural signals during the hearing phase by examining electroencephalogram (EEG) patterns of the subject for different sentences. We propose optimising the decoding process using Genetic Algorithms (GA). Six different experiments are performed on Dataset 3 of coSpeech EEG Database. Both handcrafted features and CNN-based features are used for classification. GA is used for two purposes - channel selection as well as feature selection. Two classifiers - decision trees and SVMs are used for sentence classification. A benchmark accuracy of 41.92% is obtained using the proposed methods. Accuracy improves in the alpha, beta and gamma frequency sub-bands (41.79%, 40.92%, 40.27% respectively). Channel selection using GA reduces the computational load significantly (∼ 90%) while producing comparable results (34.37%, 33.20%, 32.93% in the alpha, beta and gamma sub-bands). This work highlights that EEG is a viable, non-invasive way to decode speech from subjects, which would help people with speech disorders communicate in a better way without exertion. Silent speech decoding has applications in assisting speech-impaired individuals, ensuring private communication, and enhancing human-computer interaction.","PeriodicalId":39637,"journal":{"name":"Journal of Medical Engineering and Technology","volume":" ","pages":"471-483"},"PeriodicalIF":0.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148018033","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

Multiparametric MRI-based prostate cancer classification using transfer learning, feature fusion, and ensemble methods. 使用迁移学习、特征融合和集成方法的基于多参数mri的前列腺癌分类。

Journal of Medical Engineering and Technology Pub Date : 2026-07-01 Epub Date: 2026-05-06 DOI: 10.1080/03091902.2026.2667355

Nasser M Al-Zidi, D Vasumathi

{"title":"Multiparametric MRI-based prostate cancer classification using transfer learning, feature fusion, and ensemble methods.","authors":"Nasser M Al-Zidi, D Vasumathi","doi":"10.1080/03091902.2026.2667355","DOIUrl":"10.1080/03091902.2026.2667355","url":null,"abstract":"Prostate cancer (PCa) is a major contributor to cancer mortality in the male population globally, and precise classification of PCa into clinically significant (CS) and clinically insignificant (CiS) is essential for selecting suitable therapeutic approaches. Multiparametric magnetic resonance imaging (mpMRI) has proven to be an indispensable technique for detecting and characterising PCa lesions. This study proposes an effective framework to classify PCa using transfer learning with pre-trained VGG19 and Vision Transformer (ViT) models for feature extraction from nine mpMRI sequences of the PROSTATEx dataset, followed by feature fusion and support vector machine (SVM) classification. An ensemble approach combining multiple outputs is employed to obtain the final classification result. The proposed framework demonstrated promising results, with the ensemble output achieving an AUC score of 0.85. Key findings include the importance of incorporating higher b-values in DWI sequences and the effectiveness of combining CNN-based and transformer-based features extracted by VGG19 and ViT models. The framework has the potential to support radiologists in accurate and efficient PCa diagnosis by differentiating between CS and CiS lesions.","PeriodicalId":39637,"journal":{"name":"Journal of Medical Engineering and Technology","volume":" ","pages":"435-449"},"PeriodicalIF":0.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147843887","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

Innovative fracture-controlled safety device for osseo-integration-based prosthetic limbs. 用于骨整合义肢的创新型骨折控制安全装置。

Journal of Medical Engineering and Technology Pub Date : 2026-07-01 Epub Date: 2026-04-30 DOI: 10.1080/03091902.2026.2665154

Shaymaa S Hammoody, Kadhim K Resan, Ahmed K Muhammad, Emad K Njim, Royal Madan

{"title":"Innovative fracture-controlled safety device for osseo-integration-based prosthetic limbs.","authors":"Shaymaa S Hammoody, Kadhim K Resan, Ahmed K Muhammad, Emad K Njim, Royal Madan","doi":"10.1080/03091902.2026.2665154","DOIUrl":"10.1080/03091902.2026.2665154","url":null,"abstract":"The present study plays a crucial role in enhancing the safety and perceived quality of life for users of bone-anchored prostheses. It focuses on developing an innovative protective component using various metallic materials to identify and mitigate potential risks during use, thereby reducing the likelihood of sudden fracture and maintaining the system's structural integrity. The protective element is manufactured from Ti6Al4V alloy, while the safety pin is made from ductile cast iron. This combination allows controlled fracture of the protective element without complete separation of the prosthesis, thereby reducing the risk of falls. To optimise the numerical analysis, a 3D model of the prosthesis and its protective component was created using SolidWorks software. Loading conditions were simulated to reflect two critical phases of the gait cycle: heel strike and toe-off. The analysis revealed that the highest stress occurred during the toe-off phase, reaching 248 MPa, with a safety factor of 1.6, demonstrating the design's ability to prevent sudden failure. Tensile testing showed that ductile cast iron is a suitable material for the safety component. Although Ti6Al4V alloy surpasses it in tensile strength, ductile cast iron's lower strength ensures a controlled and less catastrophic failure under excessive loading. Numerical results confirmed a high safety factor for the protective system, indicating improved reliability and mechanical load resistance. This study presents a novel approach aimed at improving the safety of bone-anchored prostheses by minimising injury risks due to mechanical overload, ultimately enhancing user comfort and confidence.","PeriodicalId":39637,"journal":{"name":"Journal of Medical Engineering and Technology","volume":" ","pages":"415-427"},"PeriodicalIF":0.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147784529","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

Comparative machine learning for accurate EEG-based epileptic seizure state classification using sub-band analysis. 基于子带分析的基于脑电图的癫痫发作状态分类的比较机器学习。

Journal of Medical Engineering and Technology Pub Date : 2026-07-01 Epub Date: 2026-05-27 DOI: 10.1080/03091902.2026.2674650

Esraa Omran, Amro A Nour, Kosai Dabbour, Mahmoud Elloud, Muhamad Felemban

{"title":"Comparative machine learning for accurate EEG-based epileptic seizure state classification using sub-band analysis.","authors":"Esraa Omran, Amro A Nour, Kosai Dabbour, Mahmoud Elloud, Muhamad Felemban","doi":"10.1080/03091902.2026.2674650","DOIUrl":"10.1080/03091902.2026.2674650","url":null,"abstract":"Timely identification of seizure-related EEG states can support clinical assessment and motivate future monitoring tools. This study investigates a comparative machine-learning framework for EEG-based epileptic seizure state classification using sub-band analysis of the Bonn University EEG dataset. The signals are decomposed into delta, theta, alpha, beta, and gamma bands with bandpass filtering, and a set of lightweight statistical descriptors is extracted from each band. Support Vector Machines, Random Forest, XGBoost, CatBoost, Multi-Layer Perceptron, k-Nearest Neighbours, and Logistic Regression are then evaluated using multi-class ROC analysis, confusion matrices, precision, recall, F1-score, and accuracy. The results show that tree-based models, particularly XGBoost, CatBoost, and Random Forest, provide the strongest overall performance on this benchmark. The manuscript is now framed as a three-state EEG classification study rather than prospective seizure forecasting, and the discussion highlights the limited size of the Bonn dataset, the need for validation on larger datasets such as CHB-MIT and Siena Scalp EEG, and the requirements for future real-time deployment.","PeriodicalId":39637,"journal":{"name":"Journal of Medical Engineering and Technology","volume":" ","pages":"450-470"},"PeriodicalIF":0.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148036224","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

Power transition signatures of vibroarthrographic spectrograms for diagnosing knee joint pathologies. 关节振动图的功率转换特征诊断膝关节病变。

Journal of Medical Engineering and Technology Pub Date : 2026-07-01 Epub Date: 2026-05-23 DOI: 10.1080/03091902.2026.2659181

Rameesa Mol J, Jessy John

{"title":"Power transition signatures of vibroarthrographic spectrograms for diagnosing knee joint pathologies.","authors":"Rameesa Mol J, Jessy John","doi":"10.1080/03091902.2026.2659181","DOIUrl":"10.1080/03091902.2026.2659181","url":null,"abstract":"Vibroarthrographic (VAG) signals are extensively used for the diagnosis of knee joint pathologies. Since wear and tear of the cartilage affects the packing density of knee joints, pathological VAG signals are less non-stationary than healthy VAG signals. State-of-the-art feature descriptors extracted from the time-frequency (TF) representations of VAG signals do not have sufficient discrimination power to distinguish normal and abnormal VAG signals. To resolve this issue, we propose two novel features, mean of temporal dispersion of power at frequencies <math><mo>(</mo><mtext>MTDPF</mtext><mo>)</mo></math> and mean of successive temporal difference in power across frequencies <math><mo>(</mo><mtext>MSTDPF</mtext><mo>)</mo></math> computed from the spectrogram to quantify the non-stationarity of VAG signals. While <math><mrow><mtext>MTDPF</mtext></mrow></math> is the standard deviation of the spectral power across time, averaged among the frequency bins, <math><mrow><mtext>MSTDPF</mtext></mrow></math> is the average of absolute differences in spectral power across all time windows and frequency bins. <math><mrow><mtext>MTDPF</mtext></mrow></math> and <math><mrow><mtext>MSTDPF</mtext></mrow></math> are observed to be significantly different between normal and abnormal VAG signals with p-values of 0.00032 and 0.00070 respectively. <math><mrow><mtext>MTDPF</mtext></mrow></math> and <math><mrow><mtext>MSTDPF</mtext></mrow></math> are observed to be more determinant compared to the Gray Level Co-occurrence Matrix (GLCM) features and local binary patterns (LBP) features of spectrogram itself and a broad spectrum of non-stationary features extracted from other TF representations. So, these two features <math><mrow><mtext>MTDPF</mtext></mrow></math> and <math><mrow><mtext>MSTDPF</mtext></mrow></math> can be employed in the automated systems used for the interpretation of VAG signals.","PeriodicalId":39637,"journal":{"name":"Journal of Medical Engineering and Technology","volume":" ","pages":"401-414"},"PeriodicalIF":0.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148006977","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

News and product update. 新闻和产品更新。

Journal of Medical Engineering and Technology Pub Date : 2026-05-12 DOI: 10.1080/03091902.2026.2663620

引用次数: 0

Prosthetic heart valves in the aortic position: engineering aspects. 主动脉位置的人工心脏瓣膜：工程方面。

Journal of Medical Engineering and Technology Pub Date : 2026-05-01 DOI: 10.1080/03091902.2026.2659912

Dylan Goode, Ruby Dhaliwal, Dahlia Mohammadi, Hadi Mohammadi

{"title":"Prosthetic heart valves in the aortic position: engineering aspects.","authors":"Dylan Goode, Ruby Dhaliwal, Dahlia Mohammadi, Hadi Mohammadi","doi":"10.1080/03091902.2026.2659912","DOIUrl":"10.1080/03091902.2026.2659912","url":null,"abstract":"Aortic valve (AV) disease is a major contributor to cardiovascular morbidity, particularly in ageing populations. Although surgical and transcatheter AV replacement are established treatments, no current prosthesis fully reproduces the haemodynamic performance and durability of the native AV without clinical compromise. This review examines prosthetic heart valves in the aortic position from an engineering perspective, with emphasis on mechanical heart valves (MHVs) and bileaflet mechanical heart valves (BMHVs). The pathophysiology of aortic stenosis and aortic regurgitation is briefly outlined to contextualise replacement strategies, followed by a critical evaluation of bioprosthetic, mechanical, polymeric, homograft, autograft, and transcatheter valve technologies. The evolution of MHVs is reviewed alongside key considerations in material selection, including titanium alloys and pyrolytic carbon, and their associated mechanical and haemocompatibility properties. Fundamental fluid dynamic principles governing transvalvular flow, shear stress, and thrombogenicity are discussed in relation to valve geometry and hinge design. Anticoagulation requirements and their clinical implications are examined within the context of blood-material interactions. Despite significant advancements, contemporary BMHVs remain limited by non-physiological flow patterns and the lifelong need for anticoagulation therapy. Future development should prioritise improved leaflet kinematics, optimised hinge mechanics, and enhanced haemocompatibility to better approximate native valve function while reducing thromboembolic risk.","PeriodicalId":39637,"journal":{"name":"Journal of Medical Engineering and Technology","volume":" ","pages":"341-384"},"PeriodicalIF":0.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147821869","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

A multi-paradigm evaluation spanning pixels to voxels for deep learning-based kidney tumor segmentation. 基于深度学习的肾肿瘤分割中跨像素到体素的多范式评估

Journal of Medical Engineering and Technology Pub Date : 2026-05-01 Epub Date: 2026-04-15 DOI: 10.1080/03091902.2026.2651078

Rahul Lalwani, Akshada Telang, Vibha Tiwari

{"title":"A multi-paradigm evaluation spanning pixels to voxels for deep learning-based kidney tumor segmentation.","authors":"Rahul Lalwani, Akshada Telang, Vibha Tiwari","doi":"10.1080/03091902.2026.2651078","DOIUrl":"10.1080/03091902.2026.2651078","url":null,"abstract":"Automated segmentation of kidney tumors from computed tomography (CT) scans is crit- ical for diagnosis, treatment planning, and monitoring of renal cell carcinoma (RCC). While recent deep learning models report high Dice scores (>0.97), their clinical utility remains questionable due to false positive predictions that misclassify healthy tissue as tumors and computational constraints limiting real-world deployment. Unlike existing studies that emphasise quantitative metrics, this work investigates the critical gap between high segmentation accuracy and clinical applicability. We systematically evaluate six diverse architectures spanning 2D CNNs (U-Net, MedSAM) to 3D volumetric models (nnU-Net, UNETR, Total Segmenta- tor, MIScnn) on the KiTS19 dataset, emphasising false positive analysis, boundary delineation accuracy, and computational feasibility. Key findings [1]: MONAI U-Net achieves Dice score of 0.98 but exhibits excessive false positives, undermining clinical trust [2]; nnU-Net provides balanced performance (Dice: 0.82) with consistent results but demands 16GB VRAM [3]; MedSAM achieves state-of-the-art accuracy (Dice: 0.99) with minimal false positives but re- quires high-end GPUs [4]; computational constraints prevented full training of UNETR. This study identifies that high Dice scores do not guarantee clinical utility and provides actionable insights for developing clinically feasible segmentation tools for renal oncology applications including treatment planning, longitudinal monitoring, and risk assessment.","PeriodicalId":39637,"journal":{"name":"Journal of Medical Engineering and Technology","volume":" ","pages":"292-308"},"PeriodicalIF":0.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147693009","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

Parametric evaluation and optimization of a novel see-saw actuator for tremor attenuation. 一种新型跷跷板致动器的参数评估与优化。

Journal of Medical Engineering and Technology Pub Date : 2026-05-01 Epub Date: 2026-02-25 DOI: 10.1080/03091902.2026.2633336

Stephen Kimanzi, Hadi Mohammadi

引用次数: 0