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

A proximal policy optimisation algorithm-based algorithm for cardiovascular disorders detection. 基于近端策略优化算法的心血管疾病检测算法。

Journal of Medical Engineering and Technology Pub Date : 2025-02-01 Epub Date: 2025-03-11 DOI: 10.1080/03091902.2025.2471332

Yuejiao Niu, Xianchuang Fan, Rong Xue

{"title":"A proximal policy optimisation algorithm-based algorithm for cardiovascular disorders detection.","authors":"Yuejiao Niu, Xianchuang Fan, Rong Xue","doi":"10.1080/03091902.2025.2471332","DOIUrl":"10.1080/03091902.2025.2471332","url":null,"abstract":"Cardiovascular diseases (CVDs) significantly impact athletes, impacting the heart and blood vessels. This article introduces a novel method to assess CVD in athletes through an artificial neural network (ANN). The model utilises the mutual learning-based artificial bee colony (ML-ABC) algorithm to set initial weights and proximal policy optimisation (PPO) to address imbalanced classification. ML-ABC uses mutual learning to enhance the learning process by updating the positions of the food sources with respect to the best fitness outcomes of two randomly selected individuals. PPO makes updates in the ANN stable and efficient to improve the model's reliability. Our approach formulates the classification problem as a series of decision-making processes, rewarding every classification act with higher rewards for correctly identifying the instances of the minority class, hence handling class imbalance. We evaluated the model's performance on a diversified medical dataset including 26,002 athletes who were examined within the Polyclinic for Occupational Health and Sports in Zagreb, further validated with NCAA and NHANES datasets to verify generalisability. Our findings indicate that our model outperforms existing models with accuracies of 0.88, 0.86 and 0.82 for the respective datasets. These results enhance clinical model application and advance cardiovascular disorder detection and methodologies.","PeriodicalId":39637,"journal":{"name":"Journal of Medical Engineering and Technology","volume":" ","pages":"45-64"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143597978","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 protocol for the assessment of uroflowmeters. 尿流量计的评估方案。

Journal of Medical Engineering and Technology Pub Date : 2025-01-01 Epub Date: 2025-02-27 DOI: 10.1080/03091902.2025.2465708

Alexandra Bacon, Andrew Gammie

{"title":"A protocol for the assessment of uroflowmeters.","authors":"Alexandra Bacon, Andrew Gammie","doi":"10.1080/03091902.2025.2465708","DOIUrl":"10.1080/03091902.2025.2465708","url":null,"abstract":"Uroflowmetry plays an important role in the investigation of patients with lower urinary tract symptoms. We were required to assess a newly developed uroflowmeter. We thus aimed to produce a standardised protocol to test the accuracy and filtering of any new uroflowmeter. The accuracy of a newly developed uroflowmeter (Minze Uroflow®) was validated using a constant flow bottle and a cylindrical flow column. Two other machines were also tested alongside. We also assessed filtering by reproducing common artefacts in the laboratory. Finally, a questionnaire was constructed to assess the usability of the uroflowmeter by clinicians during a normal hospital flow clinic. A protocol to test new uroflowmeters was written and assessed. The protocol showed the following results for the tested uroflowmeters: a simple bench test using a constant flow bottle and cylindric column showed that the uroflow parameters (Qmax and Vvoid) were within the claimed accuracy range and ICS recommendations. The processing of the flow data by the systems effectively filtered noise, and the flow rate decline over the whole measurement range, as produced by the cylindrical flow column, was smooth and linear. Usability was assessed by clinicians in their routine clinical practice. The proposed tests meet the requirements of the ICS guidelines. We have designed a protocol which can be used by clinicians and researchers to validate the accuracy of their uroflowmeters, evaluate new models and ensure clinical usefulness.","PeriodicalId":39637,"journal":{"name":"Journal of Medical Engineering and Technology","volume":" ","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143516942","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

Transforming orthodontic retention: potential of 3D printing and biocompatible material characteristics. 改变正畸固位：3D打印的潜力和生物相容性材料特性。

Journal of Medical Engineering and Technology Pub Date : 2025-01-01 Epub Date: 2025-02-20 DOI: 10.1080/03091902.2025.2466198

Anmol Sharma, Pushpendra S Bharti

{"title":"Transforming orthodontic retention: potential of 3D printing and biocompatible material characteristics.","authors":"Anmol Sharma, Pushpendra S Bharti","doi":"10.1080/03091902.2025.2466198","DOIUrl":"10.1080/03091902.2025.2466198","url":null,"abstract":"This review article delves into the cutting-edge realm of 3D printing and its impact on the fabrication of customised orthodontic retainers, which is an essential utility in the prevention of relapse post orthodontic treatment. This review evaluates the use of biocompatible materials and provides insight into future perspectives and improvements in this field. It highlights the potential of data collecting method and 3D printing to improve orthodontic retainers' fabrication and emphasises the importance of using biocompatible materials for patient safety and efficacy. It also explains cytotoxic qualities of retainer fabrication materials, which are vital for safeguarding the oral health of the patient. The evaluation procedure enables the early diagnosis and correction of any potential difficulties, such as maladjustment or inappropriate fit, allowing for a more effective treatment. It illustrates the breakthroughs and innovations in the field of orthodontics, the advantages of 3D printing over conventional methods, as well as the advantages and disadvantages of various fabrication method. Incorporating 3D printing and review into the production of orthodontic retainers enhances the overall effectiveness and efficiency of patient treatment.","PeriodicalId":39637,"journal":{"name":"Journal of Medical Engineering and Technology","volume":" ","pages":"8-33"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143459207","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 : 2024-11-02 DOI: 10.1080/03091902.2024.2411080

John Fenner

引用次数: 0

Improving real-time physiological signs estimation using plethysmography wave and heterogeneous embedded system. 利用体积脉搏波和异构嵌入式系统改进实时生理信号估计。

Journal of Medical Engineering and Technology Pub Date : 2024-11-01 Epub Date: 2025-02-17 DOI: 10.1080/03091902.2025.2464232

Zakaria El Khadiri, Rachid Latif, Amine Saddik, Wissam Jenkal

{"title":"Improving real-time physiological signs estimation using plethysmography wave and heterogeneous embedded system.","authors":"Zakaria El Khadiri, Rachid Latif, Amine Saddik, Wissam Jenkal","doi":"10.1080/03091902.2025.2464232","DOIUrl":"10.1080/03091902.2025.2464232","url":null,"abstract":"Our work presents a real-time embedded implementation of a proposed approach for physiological signs monitoring, such as heart and breathing rates, using a Photoplethysmography signal (PPG) retrieved from digital RGB cameras. The proposed algorithm was implemented in an embedded architecture to assess both the processing time and algorithmic complexity. The proposed method is based on image processing techniques to extract the noisy PPG signal and signal processing, filtering, and decomposition algorithm to estimate the instantaneous vitals indicators. On the embedded implementation side, the common criteria that must be studied are the accuracy of the result estimation, processing time optimisation, and hardware-software adoption. The latter standard is met by the hardware-software co-design concept which will lead to adopting the algorithm's layers with an embedded platform architecture. On our side, we will principally use the High-Level Synthesis (HLS) as a parallel programming language and the computing homogeneous/heterogeneous devices (CPU/GPU). Our proposed optimised algorithm's implementation offers a gain of x5.05, x24.96, and x36.68 compared with the native version using MATLAB and the optimised version using C/C++, OpenMP, and OpenCL tool, respectively, in some functional blocks.","PeriodicalId":39637,"journal":{"name":"Journal of Medical Engineering and Technology","volume":" ","pages":"296-314"},"PeriodicalIF":0.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143442439","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

Deep transfer learning based hierarchical CAD system designs for SFM images. 基于深度迁移学习的SFM图像分层CAD系统设计。

Journal of Medical Engineering and Technology Pub Date : 2024-11-01 Epub Date: 2025-02-14 DOI: 10.1080/03091902.2025.2463580

Jyoti Rani, Jaswinder Singh, Jitendra Virmani

{"title":"Deep transfer learning based hierarchical CAD system designs for SFM images.","authors":"Jyoti Rani, Jaswinder Singh, Jitendra Virmani","doi":"10.1080/03091902.2025.2463580","DOIUrl":"10.1080/03091902.2025.2463580","url":null,"abstract":"Present work involves rigorous experimentation for classification of mammographic masses by employing four deep transfer learning models using hierarchical framework. Experimental work is carried on 518 SFM images of DDSM dataset with 208, 150 and 160 images of probably benign, suspicious- malignant and highly malignant classes, respectively. ResNet50 model is used for generating segmented mass images. For hierarchical classification framework, at node 1, the segmented mass image is classified as belonging to probably benign (BIRAD-3) class or suspicious abnormality (BIRAD-4 and BIRAD-5) class. At node 2, the segmented mass image belonging to suspicious abnormality class is further classified as suspicious malignant (BIRAD-4) class or highly malignant (BIRAD-5) class. Deep transfer learning based hierarchical CAD systems experimented in the present work include VGG16/VGG19/ GoogleNet/ResNet50 models. It was noted that deep transfer learning model VGG19 at node 1 and VGG16 at node 2, yielded highest classification accuracy of 93 % and 90 %, respectively, therefore, a deep transfer learning based hybrid hierarchical CAD system was developed by employing VGG19 at node 1 and VGG16 at node 2. This model yields overall classification accuracy of 88 %. Further, hybrid hierarchical CAD system was designed using VGG19/ANFC-LH classifier at node 1, and VGG16/ANFC-LH classifier at node 2 yielding the highest classification accuracy of 92%. The promising result yielded by hybrid hierarchical CAD system design indicates its usefulness for step-wise classification of mammographic masses.","PeriodicalId":39637,"journal":{"name":"Journal of Medical Engineering and Technology","volume":" ","pages":"279-295"},"PeriodicalIF":0.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143415617","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 : 2024-11-01 Epub Date: 2025-03-10 DOI: 10.1080/03091902.2025.2474849

J Fenner

引用次数: 0

An arrhythmia classification using a deep learning and optimisation-based methodology. 心律失常分类使用深度学习和优化为基础的方法。

Journal of Medical Engineering and Technology Pub Date : 2024-10-01 Epub Date: 2025-02-14 DOI: 10.1080/03091902.2025.2463574

Suvita Rani Sharma, Birmohan Singh, Manpreet Kaur

引用次数: 0

Hybrid attention-CNN model for classification of gait abnormalities using EMG scalogram images. 使用肌电图对步态异常进行分类的注意- cnn混合模型。

Journal of Medical Engineering and Technology Pub Date : 2024-10-01 Epub Date: 2025-02-12 DOI: 10.1080/03091902.2025.2462310

Pranshu C B S Negi, S S Pandey, Shiru Sharma, Neeraj Sharma

{"title":"Hybrid attention-CNN model for classification of gait abnormalities using EMG scalogram images.","authors":"Pranshu C B S Negi, S S Pandey, Shiru Sharma, Neeraj Sharma","doi":"10.1080/03091902.2025.2462310","DOIUrl":"10.1080/03091902.2025.2462310","url":null,"abstract":"This research aimed to develop an algorithm for classifying scalogram images generated from electromyography data of patients with Rheumatoid Arthritis and Prolapsed Intervertebral Disc. Electromyography is valuable for assessing muscle function and diagnosing neurological disorders, but limitations, such as background noise, cross-talk, and inter-subject variability complicate the interpretation and assessment. To mitigate this, the present study uses scalogram images and attention-network architecture. The algorithm utilises a combination of features extracted from an attention module and a convolution feature module, followed by classification using a Convolutional Neural Network classifier. A comparison of eight alternative architectures, including individual implementations of attention and convolution filters and a Convolutional Neural Network-only model, shows that the hybrid Convolutional Neural Network model proposed in this study outperforms the others. The model exhibits excellent discriminatory ability between gait abnormalities with an accuracy of 96.7%, a precision of 95.2%, a recall of 94.8%, and an Area Under Curve of 0.99. These findings suggest that the proposed model is highly accurate in classifying scalogram images of electromyography signals and may have significant clinical implications for early diagnosis and treatment planning.","PeriodicalId":39637,"journal":{"name":"Journal of Medical Engineering and Technology","volume":" ","pages":"239-252"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143400308","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 combination of deep learning models and type-2 fuzzy for EEG motor imagery classification through spatiotemporal-frequency features. 基于深度学习模型和2型模糊的脑电运动图像时空频率分类。

Journal of Medical Engineering and Technology Pub Date : 2024-10-01 Epub Date: 2025-02-14 DOI: 10.1080/03091902.2025.2463577

Ensong Jiang, Tangsen Huang, Xiangdong Yin

{"title":"A combination of deep learning models and type-2 fuzzy for EEG motor imagery classification through spatiotemporal-frequency features.","authors":"Ensong Jiang, Tangsen Huang, Xiangdong Yin","doi":"10.1080/03091902.2025.2463577","DOIUrl":"10.1080/03091902.2025.2463577","url":null,"abstract":"Developing a robust and effective technique is crucial for interpreting a user's brainwave signals accurately in the realm of biomedical signal processing. The variability and uncertainty present in EEG patterns over time, compounded by noise, pose notable challenges, particularly in mental tasks like motor imagery. Introducing fuzzy components can enhance the system's ability to withstand noisy environments. The emergence of deep learning has significantly impacted artificial intelligence and data analysis, prompting extensive exploration into assessing and understanding brain signals. This work introduces a hybrid series architecture called FCLNET, which combines Compact-CNN to extract frequency and spatial features alongside the LSTM network for temporal feature extraction. The activation functions in the CNN architecture were implemented using type-2 fuzzy functions to tackle uncertainties. Hyperparameters of the FCLNET model are tuned by the Bayesian optimisation algorithm. The efficacy of this approach is assessed through the BCI Competition IV-2a database and the BCI Competition IV-1 database. By incorporating type-2 fuzzy activation functions and employing Bayesian optimisation for tuning, the proposed architecture indicates good classification accuracy compared to the literature. Outcomes showcase the exceptional achievements of the FCLNET model, suggesting that integrating fuzzy units into other classifiers could lead to advancements in motor imagery-based BCI systems.","PeriodicalId":39637,"journal":{"name":"Journal of Medical Engineering and Technology","volume":" ","pages":"262-275"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143415610","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