International Journal of Online and Biomedical Engineering (iJOE)最新文献_第2页

Effectiveness of Microlearning as an Additional Teaching Instrument in Orthopaedics and Traumatology University Course 微课作为矫形外科和创伤学大学课程额外教学工具的有效性

International Journal of Online and Biomedical Engineering (iJOE) Pub Date : 2024-07-16 DOI: 10.3991/ijoe.v20i10.49543

Petar Molchovski, K. Tokmakova, D. Tokmakov

引用次数: 0

A Flexible Practicum Model on Education: Hybrid Learning Integrated Remote Laboratory Activity Design 灵活的教育实习模式：混合式学习集成远程实验室活动设计

International Journal of Online and Biomedical Engineering (iJOE) Pub Date : 2024-07-16 DOI: 10.3991/ijoe.v20i10.48031

Mardhiah Masril, N. Jalinus, Ridwan, Ambiyar, Sukardi, Dedy Irfan

{"title":"A Flexible Practicum Model on Education: Hybrid Learning Integrated Remote Laboratory Activity Design","authors":"Mardhiah Masril, N. Jalinus, Ridwan, Ambiyar, Sukardi, Dedy Irfan","doi":"10.3991/ijoe.v20i10.48031","DOIUrl":"https://doi.org/10.3991/ijoe.v20i10.48031","url":null,"abstract":"This study’s objective was to create a hybrid learning-integrated remote laboratory model with validity and practicality. This model has four learning spaces, namely live synchronous, virtual synchronous, self-paced asynchronous, and collaborative asynchronous, so it can support flexible learning. Besides that, this learning model is also based on cognitivism, connectivism, constructivism, behaviourism learning theories and Bloom’s digital taxonomy. The hybrid learning integrated remote laboratory model consists of six syntaxes: 1) issue; 2) investigation; 3) team discussion to solve problems; 4) experiment using a remote laboratory; 5) analysis and evaluation; and 6) explore new solutions. Focus group discussions (FGD) were used to collect high-quality data by seven experts in learning models, vocational education, language and technology. The hybrid learning-integrated remote laboratory model quality analysis used Aiken’s V. The result showed that the hybrid learning integratedremote laboratory model content is valid, with a validity value of 0.87. The practicality analysis result showed that the average percentage of the assessments from lecturers and students was 88.16%, so it can be concluded that it has a high validity value and is very practical.","PeriodicalId":507997,"journal":{"name":"International Journal of Online and Biomedical Engineering (iJOE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141642334","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

Lung Sound Classification for Respiratory Disease Identification Using Deep Learning: A Survey 利用深度学习进行肺部声音分类以识别呼吸系统疾病：调查

International Journal of Online and Biomedical Engineering (iJOE) Pub Date : 2024-07-16 DOI: 10.3991/ijoe.v20i10.49585

Thinira Wanasinghe, Sakuni Bandara, Supun Madusanka, D. Meedeniya, M. Bandara, Isabel De la Torre Díez

{"title":"Lung Sound Classification for Respiratory Disease Identification Using Deep Learning: A Survey","authors":"Thinira Wanasinghe, Sakuni Bandara, Supun Madusanka, D. Meedeniya, M. Bandara, Isabel De la Torre Díez","doi":"10.3991/ijoe.v20i10.49585","DOIUrl":"https://doi.org/10.3991/ijoe.v20i10.49585","url":null,"abstract":"Integrating artificial intelligence (AI) into lung sound classification has markedly improved respiratory disease diagnosis by analysing intricate patterns within audio data. This study is driven by the widespread issue of lung diseases, which affect around 500 million people globally. Early detection of respiratory diseases is crucial for delivering timely and effective treatment. Our study consists of a comprehensive survey of lung sound classification methodologies, exploring the advancements made in leveraging AI to identify and classify respiratory diseases. This survey thoroughly investigates lung sound classification models, along with data augmentation, feature extraction, explainable techniques and support tools to improve systems for diagnosing respiratory conditions. Our goal is to provide meaningful insights for healthcare professionals, researchers and technologists who are dedicated to developing methodologies for the early detection of pulmonary diseases. The paper provides a summary of the current status of lung sound classification research, highlighting both advancements and challenges in the use of AI for more accurate and efficient diagnostic methods in respiratory healthcare.","PeriodicalId":507997,"journal":{"name":"International Journal of Online and Biomedical Engineering (iJOE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141642482","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

An Algorithm for the Estimation of Hemoglobin Level from Digital Images of Palpebral Conjunctiva Based in Digital Image Processing and Artificial Intelligence 基于数字图像处理和人工智能的睑结膜数字图像血红蛋白水平估算算法

International Journal of Online and Biomedical Engineering (iJOE) Pub Date : 2024-07-16 DOI: 10.3991/ijoe.v20i10.48331

Guillermo Moreno, Abdigal Camargo, Luis Ayala, Mirko Zimic, C. del Carpio

{"title":"An Algorithm for the Estimation of Hemoglobin Level from Digital Images of Palpebral Conjunctiva Based in Digital Image Processing and Artificial Intelligence","authors":"Guillermo Moreno, Abdigal Camargo, Luis Ayala, Mirko Zimic, C. del Carpio","doi":"10.3991/ijoe.v20i10.48331","DOIUrl":"https://doi.org/10.3991/ijoe.v20i10.48331","url":null,"abstract":"Anemia is a common problem that affects a significant part of the world’s population, especially in impoverished countries. This work aims to improve the accessibility of remote diagnostic tools for underserved populations. Our proposal involves implementing algorithms to estimate hemoglobin levels using images of the eyelid conjunctiva and a calibration label captured with a mid-range cell phone. We propose three algorithms: one for calibration label segmentation, another for palpebral conjunctiva segmentation, and the last one for estimating hemoglobin levels based on the segmented images from the previous algorithms. Experiments were performed using a data set of children’s eyelid images and calibration stickers. An L1 norm error of 0.72 g/dL was achieved using the SLIC-GAT model to estimate the hemoglobin level. In conclusion, the integration of these segmentation and regression methods improved the estimation accuracy compared to current approaches, considering that the source of the images was a mid-range commercial camera. The proposed method has the potential for mass screening in low-income rural populations as it is non-invasive, and its simplicity makes it feasible for community health workers with basic training to perform the test. Therefore, this tool could contribute significantly to efforts aimed at combating childhood anemia.","PeriodicalId":507997,"journal":{"name":"International Journal of Online and Biomedical Engineering (iJOE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141641666","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

Statistical Analysis of Features for Detecting Leukemia 检测白血病特征的统计分析

International Journal of Online and Biomedical Engineering (iJOE) Pub Date : 2024-07-16 DOI: 10.3991/ijoe.v20i10.47157

Vandana Khobragade, Jagannath H. Nirmal, Aayesha Hakim

{"title":"Statistical Analysis of Features for Detecting Leukemia","authors":"Vandana Khobragade, Jagannath H. Nirmal, Aayesha Hakim","doi":"10.3991/ijoe.v20i10.47157","DOIUrl":"https://doi.org/10.3991/ijoe.v20i10.47157","url":null,"abstract":"In this age of digital microscopy, image processing, statistical analysis, categorization, and systems for decision-making have become essential tools for medical diagnostics research. By visualizing and analyzing images, clinicians can identify anomalies in intracellular structure. Leukemia is a cancerous condition marked by an unregulated increase in aberrant white blood cells (WBCs). Recognizing acute leukemia tumor cells in blood smear images (BSI) is a challenging assignment. Image segmentation is regarded as the most significant step in the automated identification of this disease. The innovative concavity-based segmentation algorithm is employed in this study to segment WBC in sub-images from the ALLIDB2 database. The concave endpoints and elliptical features are used in the segmentation step of convex-shaped cell images. The procedure involves the extraction of contour evidence, which detects the visible section of each object, and contour estimation, which corresponds to the final object’s contours. Following the identification of the cells and their internal structure by concavity-based segmentation, the cells are categorized based on their morphological and statistical features. The method was evaluated using a public dataset meant to test classification and segmentation approaches. The statistical tool SPSS is used to independently check the significance of derived features. For classification, significant features are passed into machine learning techniques such as support vector machines (SVM), k-nearest neighbor (KNN), neural networks (NN), decision trees (DT), and Nave Bayes (NB). With an AUC of 98.9% and a total accuracy of 95%, the neural network model performed better. We advocate using the neural network model to identify acute leukemia cells based on its accuracy.","PeriodicalId":507997,"journal":{"name":"International Journal of Online and Biomedical Engineering (iJOE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141641821","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

Fabrication of TiO2 Nanoparticle Coating on Stainless Steel 316L and Its Assessment for Orthopaedic Applications 不锈钢 316L 上二氧化钛纳米粒子涂层的制备及其骨科应用评估

International Journal of Online and Biomedical Engineering (iJOE) Pub Date : 2024-07-16 DOI: 10.3991/ijoe.v20i10.49177

Manjit Singh Jadon, Sandeep Kumar

{"title":"Fabrication of TiO2 Nanoparticle Coating on Stainless Steel 316L and Its Assessment for Orthopaedic Applications","authors":"Manjit Singh Jadon, Sandeep Kumar","doi":"10.3991/ijoe.v20i10.49177","DOIUrl":"https://doi.org/10.3991/ijoe.v20i10.49177","url":null,"abstract":"The study aims to investigate the efficacy of titanium dioxide (TiO2) nanoparticle coating on stainless steel 316L (SS 316L) orthopaedic implants to enhance their biocompatibility, osseointegration, and durability. The TiO2 nanoparticles were synthesized via the hydrothermal method and extensively characterized for composition, crystallinity, and morphology using techniques such as X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and scanning electron microscopy (SEM) with energy dispersive X-ray analysis (EDX), corroborated by elemental mapping. SEM and XRD analyses revealed the synthesized nanoparticles have a spherical shape and an average size of approximately 23 nanometres. The synthesized TiO2 nanoparticles were uniformly coated on SS 316L substrates using the spin coating technique, as confirmed by SEM images. Cell viability of the synthesized TiO2 nanoparticles, as well as uncoated and TiO2 nanoparticle-coated SS 316L substrates, was evaluated using the MTT (3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium bromide) assay against the NIH-3T3 mouse embryonic fibroblast cell line. The results demonstrated that the TiO2 nanoparticle-coated SS 316L substrate showed a significant increase of 22.87% in cell viability as compared to the uncoated SS 316L substrate. A ball-on-disc tribometer was employed to assess wear and friction resistance at various speeds, viz., 150 rpm, 300 rpm, and 450 rpm, under 30N load conditions for five minutes. The results collectively indicate a substantial improvement in the performance of TiO2 nanoparticle-coated SS 316L substrates for orthopaedic applications.","PeriodicalId":507997,"journal":{"name":"International Journal of Online and Biomedical Engineering (iJOE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141641690","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

e-LSTM: EfficientNet and Long Short-Term Memory Model for Detection of Glaucoma Diseases e-LSTM：用于检测青光眼疾病的高效网络和长短期记忆模型

International Journal of Online and Biomedical Engineering (iJOE) Pub Date : 2024-07-16 DOI: 10.3991/ijoe.v20i10.48603

Wiharto, Wimas Tri Harjoko, E. Suryani

{"title":"e-LSTM: EfficientNet and Long Short-Term Memory Model for Detection of Glaucoma Diseases","authors":"Wiharto, Wimas Tri Harjoko, E. Suryani","doi":"10.3991/ijoe.v20i10.48603","DOIUrl":"https://doi.org/10.3991/ijoe.v20i10.48603","url":null,"abstract":"Glaucoma is an eye disease that often has no symptoms until it is advanced. According to the World Health Organization (WHO), after cataracts, glaucoma is the second-leading cause of permanent blindness globally and is expected to affect 111.8 million patients by 2040. Early detection of glaucoma is important to reduce the risk of permanent blindness. Detection is achieved by structural measurement of early thinning of the retinal nerve fiber layer (RNFL). The RNFL is the portion of the retina located outside the optic nerve head (ONH) and can be observed in fundus images of the retina. Analysis of retinal fundus images can be performed with computer assistance using machine learning, especially deep learning. This study proposes a deep learning-based model, a convolutional neural network (CNN) using the EfficientNet architecture combined with long short-term memory (LSTM), for laucoma detection. Using ACRIMA, DRISHTI-GS, and RIM-ONE DL datasets with k-fold cross-validation, the model achieved high performance on the ACRIMA dataset: accuracy 0.9799, loss 0.0596, precision 0.9802, sensitivity 0.9799, specificity 0.9771, and F1score 0.9799. This EfficientNet and LSTM combination (e-LSTM) outperformed previous studies, offering a promising alternative for evaluating retinal fundus images in glaucoma detection.","PeriodicalId":507997,"journal":{"name":"International Journal of Online and Biomedical Engineering (iJOE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141640445","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 Alzheimer’s Disease Based on Deep Learning Using Medical Images 基于深度学习的阿尔茨海默病分类（使用医学图像

International Journal of Online and Biomedical Engineering (iJOE) Pub Date : 2024-07-16 DOI: 10.3991/ijoe.v20i10.49089

Hugo Vega-Huerta, Kevin Renzo Pantoja-Pimentel, Sebastian Yimmy Quintanilla-Jaimes, G. Maquen-Niño, Percy De-La-Cruz-VdV, Luis Guerra-Grados

{"title":"Classification of Alzheimer’s Disease Based on Deep Learning Using Medical Images","authors":"Hugo Vega-Huerta, Kevin Renzo Pantoja-Pimentel, Sebastian Yimmy Quintanilla-Jaimes, G. Maquen-Niño, Percy De-La-Cruz-VdV, Luis Guerra-Grados","doi":"10.3991/ijoe.v20i10.49089","DOIUrl":"https://doi.org/10.3991/ijoe.v20i10.49089","url":null,"abstract":"Neurodegenerative disorders, notably Alzheimer’s, pose an escalating global health challenge. Marked by the degeneration of brain neurons, these conditions lead to a gradual decline in nerve cells. Worldwide, over 55 million people grapple with dementia, with Alzheimer’s prominently impacting the aging demographic. The primary hurdle to early Alzheimer’s detection is the widespread lack of awareness. The main goal is to design and implement an artificial intelligence system using deep learning (DL) to detect Alzheimer’s disease (AD) through medical images and classify them into various stages, such as non-demented, moderate dementia, mild dementia, and very mild dementia. The dataset contains 6400 magnetic resonance images in .jpg format, with standardized dimensions of 176 × 208 pixels. To demonstrate the advantages of data augmentation and transformation techniques, four scenarios were created: two without these techniques, utilizing the Adam and SGD optimizers, and two with these techniques, also employing the Adam and SGD optimizers, respectively. The main results revealed that scenarios utilizing these techniques exhibited more stable performance when validated with a new dataset. Scenario 3, using the Adam optimizer, achieved a weighted average accuracy of 91.83%, whereas scenario 4, employing the SGD optimizer, reached 87.58% accuracy. In contrast, scenarios 1 and 2, which omitted these techniques, obtained low accuracies below 55%. It is concluded that classifying AD with a DL model exceeding 90% accuracy is feasible. This is the importance of utilizing data augmentation and transformation techniques to improve generalizability to input image variations, which is a consistent factor in the healthcare sector.","PeriodicalId":507997,"journal":{"name":"International Journal of Online and Biomedical Engineering (iJOE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141643894","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

Empowering Diabetic Eye Disease Detection: Leveraging Differential Evolution for Optimized Convolution Neural Networks 增强糖尿病眼病检测能力：利用差分进化优化卷积神经网络

International Journal of Online and Biomedical Engineering (iJOE) Pub Date : 2024-07-16 DOI: 10.3991/ijoe.v20i10.49187

Rahul Ray, Sudarson Jena, Priyadarsan Parida, Laxminarayan Dash, Sangita Kumari Biswal

{"title":"Empowering Diabetic Eye Disease Detection: Leveraging Differential Evolution for Optimized Convolution Neural Networks","authors":"Rahul Ray, Sudarson Jena, Priyadarsan Parida, Laxminarayan Dash, Sangita Kumari Biswal","doi":"10.3991/ijoe.v20i10.49187","DOIUrl":"https://doi.org/10.3991/ijoe.v20i10.49187","url":null,"abstract":"Diabetic eye detection has become a major concern across the globe, which could be effectively addressed by automated detection using a deep convolutional neural network (DCNN). CNN models have better detection and classification accuracy than other state-of-theart models. In this paper, a differential evolution (DE)-optimized CNN has been proposed for the single-step classification of diabetic retinopathy (DR) and glaucoma images. DE has been used to find out the optimized values of four hyper-parameters of CNN, i.e., the number of filters in the first layer, the filter size, the number. of convolution layers, and the number of strides. Simulation has been done using three publicly available datasets, and the accuracy obtained is 87.8%, 92.3%, and 88.7%, respectively, which outperforms other models. No other state-of-the-art model has used DE for hyper-parameter tuning in CNN models. Also, no other additional segmentation approach or handcrafted features have been used. The model has been kept simple to reduce computational costs.","PeriodicalId":507997,"journal":{"name":"International Journal of Online and Biomedical Engineering (iJOE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141643458","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

Revolutionizing Brain Tumor Analysis: A Fusion of ChatGPT and Multi-Modal CNN for Unprecedented Precision 脑肿瘤分析的革命性突破：融合 ChatGPT 和多模态 CNN，实现前所未有的精确性

International Journal of Online and Biomedical Engineering (iJOE) Pub Date : 2024-05-21 DOI: 10.3991/ijoe.v20i08.47347

Soha Rawas, A. Samala

{"title":"Revolutionizing Brain Tumor Analysis: A Fusion of ChatGPT and Multi-Modal CNN for Unprecedented Precision","authors":"Soha Rawas, A. Samala","doi":"10.3991/ijoe.v20i08.47347","DOIUrl":"https://doi.org/10.3991/ijoe.v20i08.47347","url":null,"abstract":"In this study, we introduce an innovative approach to significantly enhance the precision and interpretability of brain tumor detection and segmentation. Our method ingeniously integrates the cutting-edge capabilities of the ChatGPT chatbot interface with a state-of-the-art multi-modal convolutional neural network (CNN). Tested rigorously on the BraTS dataset, our method showcases unprecedented performance, outperforming existing techniques in terms of both accuracy and efficiency, with an impressive Dice score of 0.89 for tumor segmentation. By seamlessly integrating ChatGPT, our model unveils deep-seated insights into the intricate decision-making processes, providing researchers and physicians with invaluable understanding and confidence in the results. This groundbreaking fusion holds immense promise, poised to revolutionize the landscape of medical imaging, with far-reaching implications for clinical practice and research. Our study exemplifies the transformative potential achieved through the synergistic combination of multi-modal CNNs and natural language processing, paving the way for remarkable advancements in brain tumor detection and segmentation.","PeriodicalId":507997,"journal":{"name":"International Journal of Online and Biomedical Engineering (iJOE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141114476","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