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

Deep ensemble architecture with improved segmentation model for Alzheimer's disease detection. 基于改进分割模型的深度集成体系结构阿尔茨海默病检测。

Journal of Medical Engineering and Technology Pub Date : 2025-05-01 Epub Date: 2025-04-12 DOI: 10.1080/03091902.2025.2484691

Shilpa Jaykumar Kale, Pramod U Chavan

{"title":"Deep ensemble architecture with improved segmentation model for Alzheimer's disease detection.","authors":"Shilpa Jaykumar Kale, Pramod U Chavan","doi":"10.1080/03091902.2025.2484691","DOIUrl":"10.1080/03091902.2025.2484691","url":null,"abstract":"The most common cause of dementia, which includes significant cognitive impairment that interferes with day-to-day activities, is Alzheimer's Disease (AD). Deep learning techniques performed better on diagnostic tasks. However, current methods for detecting Alzheimer's disease lack effectiveness, resulting in inaccurate results. To overcome these challenges, a novel deep ensemble architecture for AD classification is proposed in this research. The proposed model involves key phases, including Preprocessing, Segmentation, Feature Extraction, and Classification. Initially, Median filtering is employed for preprocessing. Subsequently, an improved U-Net architecture is employed for segmentation, and then the features including Improved Shape Index Histogram (ISIH), Multi Binary Pattern (MBP), and Multi Texton are extracted from the segmented image. Then, an En-LeCILSTM is proposed, which combines the LeNet, CNN and improved LSTM models. Finally, the resultant output is obtained by averaging the intermediate output of each model, leading to improved detection accuracy. Finally, the proposed model's efficiency is assessed through various analyses, including classifier comparison, and performance metric evaluation. As a result, the En-LeCILSTM model scored a higher accuracy of 0.963 and an F-measure of 0.908, which surpasses the result of traditional methods. The outcomes demonstrate that the proposed model is notably more effective in detecting Alzheimer's disease.","PeriodicalId":39637,"journal":{"name":"Journal of Medical Engineering and Technology","volume":" ","pages":"97-121"},"PeriodicalIF":0.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144048964","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 new multimodal medical image fusion framework using Convolution Neural Networks. 基于卷积神经网络的多模态医学图像融合框架。

Journal of Medical Engineering and Technology Pub Date : 2025-05-01 Epub Date: 2025-04-11 DOI: 10.1080/03091902.2025.2488827

A Geetha Devi, Surya Prasada Rao Borra, P Rajesh Kumar

引用次数: 0

Assessment of primary stability of glenoid bone block procedures used for patients with recurrent anterior shoulder instability - a biomechanical study in a synthetic bone model. 评估用于复发性肩前路不稳患者的盂骨阻滞手术的初级稳定性-一项合成骨模型的生物力学研究

Journal of Medical Engineering and Technology Pub Date : 2025-05-01 Epub Date: 2025-04-21 DOI: 10.1080/03091902.2025.2492127

Martin Heilemann, Yasmin Youssef, Peter Melcher, Jean-Pierre Fischer, Stefan Schleifenbaum, Pierre Hepp, Jan Theopold

{"title":"Assessment of primary stability of glenoid bone block procedures used for patients with recurrent anterior shoulder instability - a biomechanical study in a synthetic bone model.","authors":"Martin Heilemann, Yasmin Youssef, Peter Melcher, Jean-Pierre Fischer, Stefan Schleifenbaum, Pierre Hepp, Jan Theopold","doi":"10.1080/03091902.2025.2492127","DOIUrl":"10.1080/03091902.2025.2492127","url":null,"abstract":"Anterior glenoid reconstruction using bone blocks is increasingly recognised as treatment option after critical bone loss. In this study, a biomechanical test setup is used to assess micromotion after bone block augmentation at the glenoid, comparing bone block augmentation with a spina-scapula block to the standard coracoid bone block (Latarjet). Twenty-four synthetic shoulder specimens were tested. Two surgical techniques (coracoid and spina-scapula bone block augmentation) were used on two different types of synthetic bone (Synbone and Sawbone). The specimens were cyclically loaded according to the 'rocking horse' setup defined in ASTM F2028. A mediolateral force of 170 N was applied on the bone block and a complete test comprised 5000 cycles. The Micromotion between bone block and glenoid was measured using a 3D Digital Image Correlation system. The measured micromotion divided into irreversible and reversible displacement of the augmented block. Medial irreversible displacement was the dominant component of the micromotion. The spina-scapula bone block showed a significantly higher irreversible displacement in medial direction compared to the coracoid block, when aggregating both types of synthetic bone (spina: 1.00 ± 0.39 mm, coracoid: 0.56 ± 0.39 mm, p = 0.01). The dominant irreversible medial displacement can be interpreted as initial settling behaviour.","PeriodicalId":39637,"journal":{"name":"Journal of Medical Engineering and Technology","volume":" ","pages":"130-137"},"PeriodicalIF":0.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144040160","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 : 2025-04-01 Epub Date: 2025-05-24 DOI: 10.1080/03091902.2025.2506951

John Fenner

引用次数: 0

Design of a patient simulator for clinicians training in mechanical ventilation: SimVep. 用于临床医生机械通气培训的病人模拟器的设计：SimVep。

Journal of Medical Engineering and Technology Pub Date : 2025-04-01 DOI: 10.1080/03091902.2025.2484672

Andres M Valencia, Ivan Ruiz, Jose I García, Alexander Galvis

{"title":"Design of a patient simulator for clinicians training in mechanical ventilation: SimVep.","authors":"Andres M Valencia, Ivan Ruiz, Jose I García, Alexander Galvis","doi":"10.1080/03091902.2025.2484672","DOIUrl":"10.1080/03091902.2025.2484672","url":null,"abstract":"Respiratory diseases are increasingly prevalent worldwide, often leading to critical conditions that require mechanical ventilation for life support. Proper management of these cases demands that clinicians be highly trained to respond effectively to various ventilatory manoeuvres during the recovery process. In this context, training tools for medical staff in mechanical ventilation become essential. Countries with emerging economies, such as Colombia, frequently face technological and economic limitations that restrict access to advanced medical training resources. As a result, the development of physical and virtual patient simulators presents a viable solution, as they can be designed using accessible technologies to support training in low-resource settings. This study presents SimVep, a patient simulator designed to emulate the physiological behaviour of obstructive and restrictive pulmonary conditions. The primary objective of SimVep is to enhance clinician training in mechanical ventilation, enabling healthcare professionals to acquire critical skills and improve patient outcomes in real-world clinical environments.","PeriodicalId":39637,"journal":{"name":"Journal of Medical Engineering and Technology","volume":" ","pages":"79-92"},"PeriodicalIF":0.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143765371","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

Classification of auditory ERPs for ADHD detection in children. 听觉erp对儿童ADHD检测的分类。

Journal of Medical Engineering and Technology Pub Date : 2025-04-01 Epub Date: 2025-03-21 DOI: 10.1080/03091902.2025.2477506

I Mercado-Aguirre, K Gutiérrez-Ruiz, S H Contreras-Ortiz

{"title":"Classification of auditory ERPs for ADHD detection in children.","authors":"I Mercado-Aguirre, K Gutiérrez-Ruiz, S H Contreras-Ortiz","doi":"10.1080/03091902.2025.2477506","DOIUrl":"10.1080/03091902.2025.2477506","url":null,"abstract":"Attention deficit hyperactivity disorder (ADHD) is one of the children's most common neurodevelopmental conditions. ADHD diagnosis is based on evaluating inattention, hyperactivity, and impulsivity symptoms that interfere with or reduce daily functioning. Although electroencephalography (EEG) tests are used for ADHD diagnosis, they are generally considered a complement to clinical evaluation. This paper proposes an approach to classify EEG records of children with ADHD and control cases. We identified and extracted relevant features from EEG signals of 47 children (22 diagnosed with ADHD and 25 controls) and evaluated machine learning techniques for classification. We used the 2-tone oddball paradigm to elicit the subjects' auditory event-related potentials (ERP), and we recorded EEG signals with a portable headset for approximately five minutes. In the feature extraction stage, we included measures from cognitive evoked potentials, frequency bands power, chaos quantification, and bispectral analysis, in addition to the age of the children and the number of high-pitched tones the children counted during the test. The SVM and Trees algorithms obtained the best performance for 86.36% accuracy and 95.45% sensitivity. These findings demonstrate the potential of portable EEG-based systems to complement standard clinical assessments, offering an objective, time-efficient, and accessible approach to support early ADHD diagnosis. Achieving high accuracy and sensitivity in classification is critical to reducing the risk of misdiagnosis and ensuring timely intervention, ultimately improving patient outcomes.","PeriodicalId":39637,"journal":{"name":"Journal of Medical Engineering and Technology","volume":" ","pages":"69-78"},"PeriodicalIF":0.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143674664","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 : 2025-03-17 DOI: 10.1080/03091902.2025.2478363

J Fenner

引用次数: 0

News and Product Update. 新闻和产品更新。

Journal of Medical Engineering and Technology Pub Date : 2025-03-10 DOI: 10.1080/03091902.2025.2474849

J Fenner

引用次数: 0

News and product update. 新闻和产品更新。

Journal of Medical Engineering and Technology Pub Date : 2025-02-01 Epub Date: 2025-04-11 DOI: 10.1080/03091902.2025.2489830

J Fenner

引用次数: 0

Quantitative evaluation of unsupervised clustering algorithms for dynamic total-body PET image analysis. 动态全身PET图像分析的无监督聚类算法的定量评价。

Journal of Medical Engineering and Technology Pub Date : 2025-02-01 Epub Date: 2025-02-20 DOI: 10.1080/03091902.2025.2466834

Oona Rainio, Maria K Jaakkola, Riku Klén

{"title":"Quantitative evaluation of unsupervised clustering algorithms for dynamic total-body PET image analysis.","authors":"Oona Rainio, Maria K Jaakkola, Riku Klén","doi":"10.1080/03091902.2025.2466834","DOIUrl":"10.1080/03091902.2025.2466834","url":null,"abstract":"Background: Recently, dynamic total-body positron emission tomography (PET) imaging has become possible due to new scanner devices. However, there is still little research systematically evaluating clustering algorithms for processing of dynamic total-body PET images.Materials and methods: Here, we compare the performance of 15 unsupervised clustering methods, including K-means either by itself or after principal component analysis (PCA) or independent component analysis (ICA), Gaussian mixture model (GMM), fuzzy c-means (FCM), agglomerative clustering, spectral clustering, and several newer clustering algorithms, for classifying time activity curves (TACs) in dynamic PET images. We use dynamic total-body 15O-water PET images of 30 patients. To evaluate the clustering algorithms in a quantitative way, we use them to classify 5000 TACs from each image based on whether the curve is taken from brain, right heart ventricle, right kidney, lower right lung lobe, or urinary bladder.Results: According to our results, the best methods are GMM, FCM, and ICA combined with mini batch K-means, which classified the TACs with a median accuracies of 89%, 83%, and 81%, respectively, in a processing time of half a second or less.Conclusion: GMM, FCM, and ICA with mini batch K-means show promise for dynamic total-body PET analysis.","PeriodicalId":39637,"journal":{"name":"Journal of Medical Engineering and Technology","volume":" ","pages":"37-44"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143459116","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