Journal of Medical Engineering and Technology最新文献_第9页

Experimental investigation on effect of accelerated speed and rotor material on life of implantable micro-infusion pump tubing 加速转速和转子材料对植入式微输液泵管材寿命影响的实验研究

Journal of Medical Engineering and Technology Pub Date : 2022-06-17 DOI: 10.1080/03091902.2022.2082575

S. Nair, A. D., Sudheesh K S

{"title":"Experimental investigation on effect of accelerated speed and rotor material on life of implantable micro-infusion pump tubing","authors":"S. Nair, A. D., Sudheesh K S","doi":"10.1080/03091902.2022.2082575","DOIUrl":"https://doi.org/10.1080/03091902.2022.2082575","url":null,"abstract":"Abstract Peristaltic pumps have been put to use in various biomedical applications like devices for the transfer of body fluids as well as devices for controlled release of medication, including implantable infusion pumps. Out of the various components of a peristaltic pump, tubing is considered the most vulnerable part. This study focuses on the performance of Silicone micro-pump tubing used in such an implantable drug delivery device. Long-term implantable medical devices are expected to be operational for about 10 years. But experimental testing of the reliability of components under normal working speeds are time-consuming and thus delays the product development cycle. While simulating the conditions in the laboratory under accelerated speeds, the effect of increasing the speed must be accounted. In this study, the effect of accelerated speed and rotor material on pump tubing life is investigated. A test jig is developed which simulates the running conditions of the infusion pump for long-duration operation. Different rotor speeds and material configurations are investigated to obtain their effect on long-duration performance. Thermal effects on the roller junctions are studied and found that the Delrin silicone combination has twice the rise in junction temperature than the titanium silicone combination. The failure modes are inspected using microstructure analysis and the best configuration is identified.","PeriodicalId":39637,"journal":{"name":"Journal of Medical Engineering and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48681488","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

Machine learning applied to healthcare: a conceptual review 机器学习在医疗保健中的应用:概念回顾

Journal of Medical Engineering and Technology Pub Date : 2022-06-09 DOI: 10.1080/03091902.2022.2080885

M. Gomes, J. Kovaleski, R. Pagani, Vander Luiz da Silva

引用次数: 2

Investigating the effects of external pressure on coronary arteries with plaques and its role in coronary artery disease 研究外压对冠状动脉斑块的影响及其在冠状动脉疾病中的作用

Journal of Medical Engineering and Technology Pub Date : 2022-06-08 DOI: 10.1080/03091902.2022.2081736

Jagath Gunasekera, Goksu Avdan, H. F. Lee, Soon-Bok Kweon, J. Klingensmith

{"title":"Investigating the effects of external pressure on coronary arteries with plaques and its role in coronary artery disease","authors":"Jagath Gunasekera, Goksu Avdan, H. F. Lee, Soon-Bok Kweon, J. Klingensmith","doi":"10.1080/03091902.2022.2081736","DOIUrl":"https://doi.org/10.1080/03091902.2022.2081736","url":null,"abstract":"Abstract The risk of an acute coronary event stems from the amount and type of plaque present, as well as the fluid and structural dynamics in the coronary artery. If the plaque’s structural stress exceeds the mechanical strength, the fibrous cap may rupture and lead to thrombosis. The patient is then likely to face a sudden myocardial infarction. An association between Coronary Heart Disease (CHD) and Sudden Cardiac Death (SCD) has been long recognised. For the first time, we are reporting a correlation between applied external pressure, such as Cardiopulmonary Resuscitation (CPR), coughing, sneezing, blowing one’s nose, etc., and diseased coronary artery plaque via 3 D coronary artery models and two-way Fluid-Solid Interaction (FSI) models. Shear and von Mises stresses inside arteries and plaques have been shown to play a major role in plaque development, progression of disease, and the likelihood of plaque rupture. Our results show a drastic change in maximum shear (300%) and von Mises stresses (500%) with increasing external pressure. This change may indicate an onset of imminent plaque rupture. Furthermore, FSI modelling indicates a strong correlation between plaque thickness, location, and external pressure. With further clinical and simulation studies, this information could be helpful in understanding potential limit pressure in the CPR process for patients with CHD.","PeriodicalId":39637,"journal":{"name":"Journal of Medical Engineering and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42997123","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: 1

Parameters of high-frequency jet ventilation using a mechanical lung model 使用机械肺模型的高频射流通气参数

Journal of Medical Engineering and Technology Pub Date : 2022-06-08 DOI: 10.1080/03091902.2022.2081370

Evgeni Kukuev, Evgeny Belugin, Dafna Willner, O. Ronen

{"title":"Parameters of high-frequency jet ventilation using a mechanical lung model","authors":"Evgeni Kukuev, Evgeny Belugin, Dafna Willner, O. Ronen","doi":"10.1080/03091902.2022.2081370","DOIUrl":"https://doi.org/10.1080/03091902.2022.2081370","url":null,"abstract":"Abstract High frequency jet ventilationis a mechanical lung ventilation method which uses a relatively high flow usually through an open system. This work examined the effect of high-frequency jet ventilation on respiratory parameters of an intubated patient simulated using a high-frequency jet ventilator attached to a ventilation monitor for measurements of ventilation parameters. The series of experiments altered specific parameters each time (respiratory rate, inspiratory-expiratory (I:E) ratio, and inspiratory pressure), under different lung compliances. A reduction of minute ventilation was observed alongside a rise in respiratory rate, with low airway pressures over the entire range of lung compliances. In addition, an I:E ratio of 2:1 to 1:1; and the tidal and minute volumes were directly related to the inspiratory pressure over all compliance settings. To conclude, the respiratory mechanics in high-frequency jet ventilation are very different from those of conventional rate ventilation in a lung model. Further studies on patients and/or a biological model are needed to investigate pCO2 and end-tidal carbon-dioxide during high-frequency jet ventilation.","PeriodicalId":39637,"journal":{"name":"Journal of Medical Engineering and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48523347","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 and deployment of cyclical focal muscle vibration system to improve walking performance in multiple sclerosis 开发和部署周期性局灶肌肉振动系统，以改善多发性硬化症患者的行走性能

Journal of Medical Engineering and Technology Pub Date : 2022-06-08 DOI: 10.1080/03091902.2022.2080880

E. Hardin, S. N. Bailey, R. Kobetic, Lisa M Lombardo, Kevin M. Foglyano, John R. Schnellenberger, S. Selkirk

{"title":"Development and deployment of cyclical focal muscle vibration system to improve walking performance in multiple sclerosis","authors":"E. Hardin, S. N. Bailey, R. Kobetic, Lisa M Lombardo, Kevin M. Foglyano, John R. Schnellenberger, S. Selkirk","doi":"10.1080/03091902.2022.2080880","DOIUrl":"https://doi.org/10.1080/03091902.2022.2080880","url":null,"abstract":"Abstract Vibration, a potent mechanical stimulus for activating muscle spindle primary afferents, may improve gait performance in persons with multiple sclerosis (MS), but has yet to be developed and deployed for multiple leg muscles with application during walking training. This study explored the development of a cyclic focal muscle vibration (FMV) system, and the deployment feasibility to correct MS walking swing phase deficits in order to determine whether this intervention warrants comprehensive study. The system was deployed during twelve, two-hour sessions of walking with cyclic FMV over six weeks. Participants served as their own control. Blood pressure, heart rate, walking speed, kinematics (peak hip, knee and ankle angles during swing), toe clearance, and step length were measured before and after deployment with blood pressure and heart rate monitored during deployment. During system deployment, there were no untoward sensations and physiological changes in blood pressure and heart rate, and volitional improvements were found in walking speed, improved swing phase kinematics, toe clearance and step length. This FMV training system was developed and deployed to improve joint flexion during walking in those with MS, and it demonstrated feasibility and benefits. Further study will determine the most effective vibration frequency and dose, carryover effects, and those most likely to benefit from this intervention.","PeriodicalId":39637,"journal":{"name":"Journal of Medical Engineering and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45466355","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

Diabetic foot thermal image segmentation using Double Encoder-ResUnet (DE-ResUnet) 基于双编码- reunet (DE-ResUnet)的糖尿病足热图像分割

Journal of Medical Engineering and Technology Pub Date : 2022-05-31 DOI: 10.1080/03091902.2022.2077997

Doha Bouallal, H. Douzi, R. Harba

{"title":"Diabetic foot thermal image segmentation using Double Encoder-ResUnet (DE-ResUnet)","authors":"Doha Bouallal, H. Douzi, R. Harba","doi":"10.1080/03091902.2022.2077997","DOIUrl":"https://doi.org/10.1080/03091902.2022.2077997","url":null,"abstract":"Abstract The use of thermography in the early diagnosis of Diabetic Foot (DF) has proven its effectiveness in identifying areas of the plantar foot that are susceptible to ulcer development. Segmentation of the foot sole is one of the most pertinent technical issues that must be performed with great precision. However, because of the inherent difficulties of foot thermal images, such as unclarity and the existence of ambiguities, segmentation approaches have not demonstrated sufficiently accurate and reliable results for clinical use. In this study, we aim to develop a fully automated, robust and accurate segmentation of the diabetic foot. To this end, we propose a deep neural network architecture adopting the encoder-decoder concept called Double Encoder-ResUnet (DE-ResUnet). This network combines the strengths of residual network and U-Net architecture. Moreover, it takes advantage of RGB (Red, Green, Blue) colour images and fuses thermal and colour information to improve segmentation accuracy. Our database consists of 398 pairs of thermal and RGB images. The population includes two groups. The first group of 54 healthy subjects. And a second group of 145 diabetic patients from the National Hospital Dos de Mayo in Peru. The dataset is splitted into 50% for training, 25% for validation and the last 25% is used for testing. This proposed model provided robust and accurate automatic segmentations of the DF and outperformed other state of the art methods with an average intersection over union (IoU) of 97%. In addition, it is able to accurately delineate the part of toes and heels which are high risk regions for ulceration.","PeriodicalId":39637,"journal":{"name":"Journal of Medical Engineering and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48760695","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: 8

Artificial intelligence optimized image segmentation techniques for renal cyst detection 肾囊肿检测的人工智能优化图像分割技术

Journal of Medical Engineering and Technology Pub Date : 2022-05-31 DOI: 10.1080/03091902.2022.2080882

Bhawna Dhruv, Neetu Mittal, Megha Modi

{"title":"Artificial intelligence optimized image segmentation techniques for renal cyst detection","authors":"Bhawna Dhruv, Neetu Mittal, Megha Modi","doi":"10.1080/03091902.2022.2080882","DOIUrl":"https://doi.org/10.1080/03091902.2022.2080882","url":null,"abstract":"Abstract The vast number of image modalities available nowadays has given rise and access to a number of medical images. These images perhaps suffer issues such as low contrast, noise, ill-defined boundaries and poor visualisation. Therefore, a need for effective segmentation arises. Medical image segmentation plays a significant role in identifying a disorder, treatment planning, routine follow ups and computer-guided surgery respectively. The paper presents automatic medical image segmentation to overcome the imaging concerns and demarcate each notch & boundary in an image. The proposed algorithm identifies the existing kidney cyst precisely as they may be related to extreme disorders that may affect kidney function. The algorithm has been further tested on automatic segmentation using Genetic Algorithm, Ant Colony Optimisation and Fuzzy C Means Clustering. In terms of visualisation of valuable pathology, GA stands out and further helps in better assessment of the extent of the disease providing with better representation of the kidney cysts thereby giving a better diagnostic assurance and understanding of the nature of any disorder helping the medical practitioners as well as the patients. Experimental results on segmentation of kidney CT images conclusively demonstrate that the Genetic Algorithm is much more effective and robust.","PeriodicalId":39637,"journal":{"name":"Journal of Medical Engineering and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49204108","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

Machine learning based COVID -19 disease recognition using CT images of SIRM database 基于机器学习的基于SIRM数据库CT图像的COVID-19疾病识别

Journal of Medical Engineering and Technology Pub Date : 2022-05-31 DOI: 10.1080/03091902.2022.2080883

S. Pandey, R. Janghel, P. Mishra, Rachana Kaabra

{"title":"Machine learning based COVID -19 disease recognition using CT images of SIRM database","authors":"S. Pandey, R. Janghel, P. Mishra, Rachana Kaabra","doi":"10.1080/03091902.2022.2080883","DOIUrl":"https://doi.org/10.1080/03091902.2022.2080883","url":null,"abstract":"Abstract The COVID-19 pandemic, probably one of the most widespread pandemics humanity has encountered in the twenty first century, caused death to almost 1.75 M people worldwide, impacting almost 80 M lives with direct contact. In order to contain the spread of coronavirus, it is necessary to develop a reliant and quick method to identify those who are affected and isolate them until full recovery is made. The imagery knowledge has been shown to be useful for quick COVID-19 diagnosis. Though the scans of computational tomography (CT) demonstrate a range of viral infection signals, considering the vast number of images, certain visual characteristics are challenging to distinguish and can take a long time to be identified by radiologists. In this study for detection of the COVID-19, a dataset is formed by taking 3764 images. The feature extraction process is applied to the dataset to increase the classification performance. Techniques like Grey Level Co-occurrence Matrix (GLCM) and Discrete Wavelet Transform (DWT) are used for feature extraction. Then various machine learning algorithms applied such as Support Vector Machines (SVM), Linear Discriminant Analysis (LDA), Multi- Level Perceptron, Naive Bayes, K-Nearest Neighbours and Random Forests are used for classification of COVID-19 disease detection. Sensitivity, Specificity, Accuracy, Precision, and F-score are the metrics used to measure the performance of different machine learning models. Among these machine learning models SVM with GLCM as feature extraction technique using 10-fold cross validation gives the best classification result with 99.70% accuracy, 99.80% sensitivity and 97.03% F-score. We also ran these tests on different data sets and found that the results are similar across those too, as discussed later in the results section.","PeriodicalId":39637,"journal":{"name":"Journal of Medical Engineering and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42089796","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: 2

Evaluation of gastric emptying in critically ill patients using electrical impedance method: a pilot study 电阻抗法评价危重病人胃排空:一项初步研究

Journal of Medical Engineering and Technology Pub Date : 2022-05-11 DOI: 10.1080/03091902.2022.2059116

A. Basher, M. Moniruzzaman, Md Maruful Islam, M. Rashid, I. Chowdhury, Akhtaruzzaman Akm, K. S. Rabbani

{"title":"Evaluation of gastric emptying in critically ill patients using electrical impedance method: a pilot study","authors":"A. Basher, M. Moniruzzaman, Md Maruful Islam, M. Rashid, I. Chowdhury, Akhtaruzzaman Akm, K. S. Rabbani","doi":"10.1080/03091902.2022.2059116","DOIUrl":"https://doi.org/10.1080/03091902.2022.2059116","url":null,"abstract":"Abstract Nasogastric feeding is commonly used to deliver enteral feed in critically ill patients and several methods are used for assessing the gastric residual volume with limitations. A new approach for gastric emptying time measurement has been developed using Electric Impedance Method (EIM). The study aims to establish whether EIM is useful for measuring gastric emptying during nasogastric feeding compared with nasogastric suction. The pilot study was performed among the patients in the Intensive Care Unit (ICU), Bangladesh, from 2018 to 2019. Enteral feed was given to patients by NG tube. Gastric emptying and Gastric Residual Volume (GRV) were measured using EIM and nasogastric suction tube. Patterns of filling and emptying were almost the same in all subjects but emptying time varied between individuals that correlated well with GRV in 16 patients. Therefore, the study showed that the measurement of gastrc volume by the non-invasive and hazard-free electrical impedance method has a high specificity (90%) and efficacy of 80%. The study also revealed significant changes in gastric emptying time due to different body statuses. EIM seemed to be capable of measuring gastric emptying over time. EIM could become a standard tool for monitoring gastric emptying in patients at risk of gastroparesis.","PeriodicalId":39637,"journal":{"name":"Journal of Medical Engineering and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43223223","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: 1

Classification of lung sounds using scalogram representation of sound segments and convolutional neural network. 利用声段尺度图和卷积神经网络对肺音进行分类。

Journal of Medical Engineering and Technology Pub Date : 2022-05-01 Epub Date: 2022-02-25 DOI: 10.1080/03091902.2022.2040624

Huong Pham Thi Viet, Huyen Nguyen Thi Ngoc, Vu Tran Anh, Huy Hoang Quang

{"title":"Classification of lung sounds using scalogram representation of sound segments and convolutional neural network.","authors":"Huong Pham Thi Viet, Huyen Nguyen Thi Ngoc, Vu Tran Anh, Huy Hoang Quang","doi":"10.1080/03091902.2022.2040624","DOIUrl":"https://doi.org/10.1080/03091902.2022.2040624","url":null,"abstract":"<p><p>Lung auscultation is one of the most common methods for screening of lung diseases. The increasingly high rate of respiratory diseases leads to the need for robust methods to detect the abnormalities in patients' breathing sounds. Lung sounds analysis stands out as a promising approach to automatic screening of lung diseases, serving as a second opinion for doctors as a stand-alone device for preliminary screening of lung diseases in remote areas. In previous research on lung classification using ICBHI Database on Kaggle, lung audios are converted to spectral images and fed into deep neural networks for training. There are a few studies which uses the scalogram, however they focussed on classification among different lung diseases. The use of scalograms in categorising the sound types are rarely used. In this paper, we combined scalograms and neural networks for classification of lung sound types. Padding methods and augmentation are also considered to evaluate the impacts on classification score. An ensemble learning is incorporated to increase classification accuracy by utilising voting of many models. The model trained and evaluated has shown prominent improvement of this method on classification on the benchmark ICBHI database.</p>","PeriodicalId":39637,"journal":{"name":"Journal of Medical Engineering and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39960614","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: 3