Journal of Medical Engineering and Technology最新文献

{"title":"Predicting the influence of yoga on chronic venous insufficiency utilizing the Multi-Layer Perceptron Classifier.","authors":"Huawei Liu","doi":"10.1080/03091902.2025.2471331","DOIUrl":"https://doi.org/10.1080/03091902.2025.2471331","url":null,"abstract":"<p><p>It further zeroes in on the forecasting of the effects of yoga on CVI with the aid of a broad dataset including demographic background, basic case severities, and yoga practice details. Through careful feature engineering, the machine learning algorithms foresee such eventualities as the changes in the symptom severity and overall improvements in well-being. This predictive model has the potential to transform personalised treatment approaches in CVI by providing specific yoga practice recommendations, optimising therapeutic methods, and enhancing the effective utilisation of health resources. It is also emphasised that ethical considerations, patient preferences, and safety issues are of utmost importance and must be ensured in any responsible clinical implementation. Integrating MLPC with optimisation systems holds great promise as a novel approach. This integration is likely to provide a befitting platform for the customised management of CVI and give essential insights for ongoing and future healthcare service practices. Certainly, results across VCSS-PRE and VCSS-1 revealed remarkable performance that the MLPC+MGO model achieved in prediction and classification. The results depict that this model ensured impressive levels of both Accuracy and Precision through all the layers of the MLPC. On that account, the first layer obtained top results, with a result of 0.957 Accuracy and 0.961 Precision for VCSS-PRE, and even more at results of 0.971 Accuracy and 0.973 Precision for VCSS-1.</p>","PeriodicalId":39637,"journal":{"name":"Journal of Medical Engineering and Technology","volume":" ","pages":"1-23"},"PeriodicalIF":0.0,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144327114","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":"Pre- and post-fixation comparative study on the rehabilitation of transfemoral amputees using a patient-specific polycentric knee joint.","authors":"Vaibhav Jaiswal, Subramani Kanagaraj","doi":"10.1080/03091902.2025.2514563","DOIUrl":"https://doi.org/10.1080/03091902.2025.2514563","url":null,"abstract":"<p><p>Rehabilitation of transfemoral amputees remains a societal challenge due to the absence of natural knee joint motion. Despite progress in high-end prosthetic knee joints, issues of affordability, functionality, and patient-specific fitting persist. This study addresses these concerns through an indigenously developed, patient-specific configurable polycentric knee joint with improved functionalities. Five transfemoral amputees and ten healthy controls participated. The prosthesis is fitted to amputees, followed by a 12-week rehabilitation program. Pre- and post-fixation assessments are conducted using SF-36 and QTFA-70 to evaluate health-related quality of life (HRQL) and functionality. Kinematic and dynamic analyses during daily activities are performed using a high-speed video camera, Kinovea software, manual goniometer, and force plate. Results show a 55% improvement in HRQL and 88% improvement in global performance post-fixation. The measured knee flexion angles are 47.6°±5.9° (swing phase), 131.4°±6.6° (deep squat), 112.8°±5° (floor sitting), 125.1°±5.4° (chair sitting), and 99.2°±4.5° (bent knee sitting), closely matching healthy controls. Peak vertical ground reaction forces and gait symmetry also align with sound limbs and controls. These outcomes demonstrate the prosthetic design's potential in restoring near-anatomical motion and significantly improving the functional performance of transfemoral amputees.</p>","PeriodicalId":39637,"journal":{"name":"Journal of Medical Engineering and Technology","volume":" ","pages":"1-19"},"PeriodicalIF":0.0,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144318301","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 and critical assessment of analytical solutions of forced-damped vibrations of the cardiovascular-implant system.","authors":"Kuntal Kumar Das, Yogendra Srivastava, Bikramjit Basu, Ashutosh Kumar Dubey","doi":"10.1080/03091902.2025.2508230","DOIUrl":"https://doi.org/10.1080/03091902.2025.2508230","url":null,"abstract":"<p><p>A recent innovation in bioelectronic medicine is the use of implantable devices capable of harvesting biomechanical energy from cardiac motion. Such self-powered devices would facilitate cardiovascular functionality in patients with compromised hearts. This not only requires integrating bioelectronic medicine with cardiovascular physiology, but also a quantitative predictability of their functioning. We present a first attempt to establish a quantitative basis derived through biophysical considerations. Assuming cardiac functionality to be described using a spring-dashpot model, we present analytical solutions for different scenarios of physiological relevance. A key result is that the inverse lifetime lower than the natural frequency of the heart vibration leads to a rapid decrease in vibrational amplitudes of the implant as the cardiac cycle moves to the relaxation phase. When the inverse lifetime equals the natural frequency, vibrations persist to the largest extent and a substantial amount of energy can be harvested in a cardiac cycle via energy harvesting mechanisms (piezoelectric and triboelectric). Our analysis points to the critical role of the implant mass on variations in displacement during heart vibrations. Our theoretical predictions provide guidelines for developing next-generation biomedical devices with the heart as the <i>in vivo</i> source of energy harvesting.</p>","PeriodicalId":39637,"journal":{"name":"Journal of Medical Engineering and Technology","volume":" ","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144250123","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":"Multi-stage generative adversarial network model for segmenting retinal vascular structures in eye disease prediction.","authors":"Roshan S Bhanuse, Ganesh Yenurkar, Kavita R Singh, Sandip Mal, Sulakshana B Mane, Rahul Kachhwah, Neeraj Rajbhar, Saksham Take, Tejas Thakre","doi":"10.1080/03091902.2025.2509275","DOIUrl":"https://doi.org/10.1080/03091902.2025.2509275","url":null,"abstract":"<p><p>Retinal vessel segmentation is essential for precise ophthalmological diagnoses, particularly in the prediction of retinal degenerative diseases. However, existing methods usually lack robustness and accuracy, especially in segmentation of thin or overlapping vessels. To face these challenges, this study introduces an enhanced retina-RV-Gain segmentation model, which employs an architecture of various stages to refine the results of segmentation iteratively. The model integrates attention mechanisms to better capture complex vessel structures and employs an adaptive loss function to manage class imbalance. In addition, a specially designed discriminator enhances the model's ability to distinguish fine details from background noise vessels. The proposed RV-Gan is trained in comprehensive data sets that comprise retinal images, segmentation masks and noted labels, including Stare-DB, Chase-DB1 and Drive, using the Python platform. Experimental results demonstrate a segmentation accuracy of up to 99% in normal, abnormal and base vessels. These findings highlight the potential of the model to significantly improve diagnostic accuracy and support early prediction of disease in clinical ophthalmology. Overall, the enhanced RV-Gan architecture offers a robust solution to the limitations of current approaches, providing segmentation of high fidelity retinal vessels and advancing the predictive analysis of retinal degenerative conditions.</p>","PeriodicalId":39637,"journal":{"name":"Journal of Medical Engineering and Technology","volume":" ","pages":"1-26"},"PeriodicalIF":0.0,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144162744","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":"Detection of diabetic peripheral neuropathy from index finger using vibration mechanism.","authors":"Vijay Dave, Yash Patel","doi":"10.1080/03091902.2025.2508229","DOIUrl":"https://doi.org/10.1080/03091902.2025.2508229","url":null,"abstract":"<p><p><b>Objective:</b> Diabetic Peripheral Neuropathy (DPN) is the most common prolonged complication of diabetes. A nerve reaches to the hands, legs, and a foot is damaged due to excessive glucose level. This leads to the loss of sensation, numbness and pain in the feet, legs or hands. Currently available devices are expensive, take more time and need more expertise to operate them to detect the level of DPN. This study is designed to detect the level of diabetic peripheral neuropathy (DPN) from first joint of index finger using a novel 128-Hz electronic tuning fork prototype which is capable of performing accurate vibration perception duration (VPD). <b>Methods:</b> A total of 169 diabetic patients were recruited from the secondary author's practice for assessment of level of DPN with our device. All the patients were enrolled according to an approved protocol. Patient places index finger on the tip of our device in such a way that the tip covers the first joint of index finger. Our device then provides the vibration of desired frequency and voltage to the index finger <i>via</i> tactile platform and patient starts feeling the vibration. Depending on the vibration perception duration (VPD) for which the patient feels the vibration, 4 levels of DPN i.e. Normal, Mild, Moderate and Severe are calculated. Three repeated measurements were taken from all 169 patients. <b>Results:</b> Our device detected 74 DPN patients (6 severe, 26 moderates, 42 mild) and 89 normal (no DPN) patients. The mean of vibration perception duration (VPD) was 6.8 s, with a standard deviation (SD) of ± 0.84 s of all 169 patients. Mean VPD of severe, moderate, mild and normal level of DPN patients was 1.73 (mean SD = 0.7 s), 5.82 (mean SD = 0.84 s), 8.32 (mean SD = 1 s) and 11.3 s (mean SD = 0.84 s), respectively. Considering the Biothesiometer as the reference standard, our results were compared against it and our device's result accuracy was > 92%. <b>Conclusion:</b> VPD was a sensitive measure of a detection of level of DPN. The device is compact, handy, easy to use and takes only few seconds to diagnose the level of DPN level in diabetic patients.</p>","PeriodicalId":39637,"journal":{"name":"Journal of Medical Engineering and Technology","volume":" ","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144143909","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":"A detailed review of the recent development of needle-free drug delivery devices.","authors":"Ayushman Srivastava, Abhishek Kundu, Akshoy Ranjan Paul","doi":"10.1080/03091902.2025.2508893","DOIUrl":"https://doi.org/10.1080/03091902.2025.2508893","url":null,"abstract":"<p><p>This study aims to highlight the noteworthy impression of the needle-free drug delivery devices to endorse drug delivery technology innovation. By briefing existing information, this assessment can guide the development of a new device. A thorough literature survey has been done to analyse the design, technology mechanism, CFD studies, clinical results, and patents filed in the field of such devices. Challenges and future scope of improvement in the existing devices were reported. A number of drug delivery devices were investigated and have been reported in this study. Among all the reported devices, the shock wave-operated device has the ability to reduce the current limitations in needle-free drug delivery device, offering a usable solution for treating diseases. Most devices were developed for liquid vaccination, and trials were done both on animals and humans. Clinical trial evidence shows that these systems were acceptable to clinicians as well as patients. Several parameters can be modified to attain the required depth of penetration under the skin.</p>","PeriodicalId":39637,"journal":{"name":"Journal of Medical Engineering and Technology","volume":" ","pages":"1-20"},"PeriodicalIF":0.0,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144143854","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":"Comparison of seven machine learning models in hypertension classification using photoplethysmographic and anthropometric data.","authors":"Alessandro Gentilin","doi":"10.1080/03091902.2025.2506419","DOIUrl":"https://doi.org/10.1080/03091902.2025.2506419","url":null,"abstract":"<p><p>This study presents an algorithm for classifying individuals into four hypertension categories (healthy, prehypertension, Stage 1, and Stage 2) using indices computed from photoplethysmographic (PPG) and anthropometric data. The dataset includes 219 individuals (115 women, 104 men, ages 21-86), with resting PPG signals, body mass index (BMI), age, weight, height, and resting heart rate. Key features (PPGAI, Ab, and Ad indices) were computed from the PPG signal. After dimensionality reduction through stepwise linear regression, the most informative predictors of hypertensive stages were identified for model training. Seven machine learning models, including Support Vector Machine (SVM), K-Nearest Neighbours, Logistic Regression, Random Forest, Naive Bayes, Linear Discriminant Analysis, and Quadratic Discriminant Analysis, were evaluated using leave-one-out cross-validation and the most accurate one was selected for final classification. The Linear SVM showed the best performance, correctly classifying 71.3%, 67.1%, 38.2%, and 55% of healthy, prehypertensive, Stage 1, and Stage 2 subjects, respectively. However, in a preliminary screening scenario aimed at prompting clinical follow-up for positive cases, the algorithm flagged 76.5% of prehypertensive, 97.1% of Stage 1, and 100% of Stage 2 individuals as belonging to one of the three hypertensive categories. Nonetheless, additional training data are needed to improve the model's accuracy.</p>","PeriodicalId":39637,"journal":{"name":"Journal of Medical Engineering and Technology","volume":" ","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144143862","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":"Strain shielding effect analysis of solid and porous Ti-6Al-4V alloy implanted femur bone using finite element analysis.","authors":"Sita Ram Modi, Amardeep Dongare, Kailash Jha","doi":"10.1080/03091902.2025.2498748","DOIUrl":"https://doi.org/10.1080/03091902.2025.2498748","url":null,"abstract":"<p><p>In the proposed work, strain shielding effect analysis of solid and porous Ti-6Al-4V alloy implanted femur bone using finite element analysis is carried out. Strain shielding is a significant concern during total hip arthroplasty (THA) since it reduces bone growth and results in aseptic implant loosening due to the mismatch of femur and implant characteristics. The study examined solid and porous implanted femur bone under three loading conditions: standing, walking and stair climbing. The results show that strains on bone due to porous implants as compared to solid implants have been increased by 31, 24.3% and reduced by 12.18% for standing, walking, and stair climbing human activities, respectively. The findings show that porous implants promote bone growth and reduce aseptic implant loosening by lowering the strain and stress shielding effect.</p>","PeriodicalId":39637,"journal":{"name":"Journal of Medical Engineering and Technology","volume":" ","pages":"1-14"},"PeriodicalIF":0.0,"publicationDate":"2025-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144048969","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":"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":"https://doi.org/10.1080/03091902.2025.2492127","url":null,"abstract":"<p><p>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, <i>p</i> = 0.01). The dominant irreversible medial displacement can be interpreted as initial settling behaviour.</p>","PeriodicalId":39637,"journal":{"name":"Journal of Medical Engineering and Technology","volume":" ","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2025-04-21","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}

{"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":"https://doi.org/10.1080/03091902.2025.2484691","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":39637,"journal":{"name":"Journal of Medical Engineering and Technology","volume":" ","pages":"1-25"},"PeriodicalIF":0.0,"publicationDate":"2025-04-12","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}