Pub Date : 2024-02-14DOI: 10.3991/ijoe.v20i02.42883
Arturo Gago, Jean Marko Aguirre, Lenis Wong
Breast cancer is one of the most significant global health challenges. Effective diagnosis and prognosis prediction are crucial for improving patient outcomes in the case of this disease. As machine learning (ML) has significantly improved prediction models in many disciplines, the goal of this study is to develop a ML system for medical specialists that can accurately predict tumor diagnosis and patient survival for breast cancer patients. For the training of diagnosis and survival prediction, five algorithmic models—decision tree (DT), random forest (RF), naive bayes (NB), support vector machines (SVMs), and gradient boosting—were trained with 569 records from the Breast Cancer Wisconsin dataset and 1,980 records from the Breast Cancer Gene Expression Profiles dataset. The results showed that the NB model exhibited better performance for tumor diagnosis, achieving an accuracy of 95.0%, while RF presented the best results for patient survival, with an accuracy of 76.0%. A survey of medical experts’ experience with the resulting system showed high scores in reliability, performance, satisfaction, usability, and efficiency, confirming that ML systems have the potential to improve breast cancer patient outcomes.
乳腺癌是全球健康面临的最重大挑战之一。有效的诊断和预后预测对于改善这种疾病的患者预后至关重要。由于机器学习（ML）在许多学科中都极大地改进了预测模型，因此本研究的目标是为医学专家开发一种 ML 系统，以准确预测乳腺癌患者的肿瘤诊断和生存期。在诊断和生存预测的训练中，使用了五种算法模型--决策树（DT）、随机森林（RF）、奈夫贝叶斯（NB）、支持向量机（SVM）和梯度提升--对威斯康星乳腺癌数据集的 569 条记录和乳腺癌基因表达谱数据集的 1,980 条记录进行了训练。结果表明，NB 模型在肿瘤诊断方面表现更佳，准确率达到 95.0%，而 RF 模型在患者存活率方面表现最佳，准确率达到 76.0%。对医学专家使用该系统的经验进行的调查显示，该系统在可靠性、性能、满意度、可用性和效率方面都获得了高分，这证实了 ML 系统具有改善乳腺癌患者预后的潜力。
{"title":"Machine Learning System for the Effective Diagnosis and Survival Prediction of Breast Cancer Patients","authors":"Arturo Gago, Jean Marko Aguirre, Lenis Wong","doi":"10.3991/ijoe.v20i02.42883","DOIUrl":"https://doi.org/10.3991/ijoe.v20i02.42883","url":null,"abstract":"Breast cancer is one of the most significant global health challenges. Effective diagnosis and prognosis prediction are crucial for improving patient outcomes in the case of this disease. As machine learning (ML) has significantly improved prediction models in many disciplines, the goal of this study is to develop a ML system for medical specialists that can accurately predict tumor diagnosis and patient survival for breast cancer patients. For the training of diagnosis and survival prediction, five algorithmic models—decision tree (DT), random forest (RF), naive bayes (NB), support vector machines (SVMs), and gradient boosting—were trained with 569 records from the Breast Cancer Wisconsin dataset and 1,980 records from the Breast Cancer Gene Expression Profiles dataset. The results showed that the NB model exhibited better performance for tumor diagnosis, achieving an accuracy of 95.0%, while RF presented the best results for patient survival, with an accuracy of 76.0%. A survey of medical experts’ experience with the resulting system showed high scores in reliability, performance, satisfaction, usability, and efficiency, confirming that ML systems have the potential to improve breast cancer patient outcomes.","PeriodicalId":507997,"journal":{"name":"International Journal of Online and Biomedical Engineering (iJOE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139779452","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}
Pub Date : 2024-02-14DOI: 10.3991/ijoe.v20i02.46789
Diego Alberto Cadillo-Laurentt, Ernesto Paiva-Peredo
Breast cancer is currently one of the most diagnosed oncological diseases worldwide, with thousands of new cases per year. Early detection and identifying its progression are key to overcoming the mortality rate. A recurrent test, to determine how far the disease has spread throughout the patient’s body, is the histological analysis of the sentinel lymph node near the breast. Although an expert pathologist performs this, it is usually an exhausting and time-consuming task, with a high possibility of error. This work presents a method to detect breast cancer metastasis through histological imaging of sentinel lymph nodes using convolutional neural networks. In this study, the performance of three models DenseNet-121, DenseNet-169 and DenseNet-201 are tested and compared. Experimental results indicated that the accuracy, precision, sensitivity and specificity (97.93%, 97.4%, 97.48% and 98.24%) of DenseNet-201 could reduce pathologist errors during the diagnostic process or serve as a second opinion tool.
{"title":"Histopathological Image Classification Using Convolutional Neural Networks for Detection of Metastatic Breast Cancer in Lymph Nodes","authors":"Diego Alberto Cadillo-Laurentt, Ernesto Paiva-Peredo","doi":"10.3991/ijoe.v20i02.46789","DOIUrl":"https://doi.org/10.3991/ijoe.v20i02.46789","url":null,"abstract":"Breast cancer is currently one of the most diagnosed oncological diseases worldwide, with thousands of new cases per year. Early detection and identifying its progression are key to overcoming the mortality rate. A recurrent test, to determine how far the disease has spread throughout the patient’s body, is the histological analysis of the sentinel lymph node near the breast. Although an expert pathologist performs this, it is usually an exhausting and time-consuming task, with a high possibility of error. This work presents a method to detect breast cancer metastasis through histological imaging of sentinel lymph nodes using convolutional neural networks. In this study, the performance of three models DenseNet-121, DenseNet-169 and DenseNet-201 are tested and compared. Experimental results indicated that the accuracy, precision, sensitivity and specificity (97.93%, 97.4%, 97.48% and 98.24%) of DenseNet-201 could reduce pathologist errors during the diagnostic process or serve as a second opinion tool.","PeriodicalId":507997,"journal":{"name":"International Journal of Online and Biomedical Engineering (iJOE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139778157","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}
Pub Date : 2024-02-14DOI: 10.3991/ijoe.v20i02.43795
S. Hamida, Driss Lamrani, Mohammed Amine Bouqentar, Oussama El Gannour, B. Cherradi
In order to effectively treat skin diseases, an accurate and prompt diagnosis is required. In this article, a novel method for classifying skin disorders using a multimodal classifier is presented. The proposed classifier utilizes multiple information sources to enhance the accuracy of disease classification. It incorporates images of skin lesions and patient-specific data. The multimodal classifier simultaneously classifies diseases by combining image and structured data inputs. The effectiveness of the proposed classifier was evaluated using the ISIC 2018 dataset, which includes images and clinical data for seven categories of skin diseases. The results indicate that the proposed model outperforms conventional single-modal and single-task classifiers, achieving an accuracy of 98.66% for image classification and 94.40% for clinical data classification. In addition, we compare the performance of the proposed model with that of other methodologies, demonstrating its superiority. Despite yielding promising results, the proposed method has limitations in terms of data requirements and generalizability. Future research directions include incorporating additional information sources, investigating genetic data integration, and applying the method to various medical conditions. This study illustrates the potential of integrating multimodal techniques with transfer learning in deep neural networks to enhance the classification accuracy of cutaneous diseases.
{"title":"An Integrated Multimodal Deep Learning Framework for Accurate Skin Disease Classification","authors":"S. Hamida, Driss Lamrani, Mohammed Amine Bouqentar, Oussama El Gannour, B. Cherradi","doi":"10.3991/ijoe.v20i02.43795","DOIUrl":"https://doi.org/10.3991/ijoe.v20i02.43795","url":null,"abstract":"In order to effectively treat skin diseases, an accurate and prompt diagnosis is required. In this article, a novel method for classifying skin disorders using a multimodal classifier is presented. The proposed classifier utilizes multiple information sources to enhance the accuracy of disease classification. It incorporates images of skin lesions and patient-specific data. The multimodal classifier simultaneously classifies diseases by combining image and structured data inputs. The effectiveness of the proposed classifier was evaluated using the ISIC 2018 dataset, which includes images and clinical data for seven categories of skin diseases. The results indicate that the proposed model outperforms conventional single-modal and single-task classifiers, achieving an accuracy of 98.66% for image classification and 94.40% for clinical data classification. In addition, we compare the performance of the proposed model with that of other methodologies, demonstrating its superiority. Despite yielding promising results, the proposed method has limitations in terms of data requirements and generalizability. Future research directions include incorporating additional information sources, investigating genetic data integration, and applying the method to various medical conditions. This study illustrates the potential of integrating multimodal techniques with transfer learning in deep neural networks to enhance the classification accuracy of cutaneous diseases.","PeriodicalId":507997,"journal":{"name":"International Journal of Online and Biomedical Engineering (iJOE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139836896","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}
Pub Date : 2024-02-14DOI: 10.3991/ijoe.v20i02.43795
S. Hamida, Driss Lamrani, Mohammed Amine Bouqentar, Oussama El Gannour, B. Cherradi
In order to effectively treat skin diseases, an accurate and prompt diagnosis is required. In this article, a novel method for classifying skin disorders using a multimodal classifier is presented. The proposed classifier utilizes multiple information sources to enhance the accuracy of disease classification. It incorporates images of skin lesions and patient-specific data. The multimodal classifier simultaneously classifies diseases by combining image and structured data inputs. The effectiveness of the proposed classifier was evaluated using the ISIC 2018 dataset, which includes images and clinical data for seven categories of skin diseases. The results indicate that the proposed model outperforms conventional single-modal and single-task classifiers, achieving an accuracy of 98.66% for image classification and 94.40% for clinical data classification. In addition, we compare the performance of the proposed model with that of other methodologies, demonstrating its superiority. Despite yielding promising results, the proposed method has limitations in terms of data requirements and generalizability. Future research directions include incorporating additional information sources, investigating genetic data integration, and applying the method to various medical conditions. This study illustrates the potential of integrating multimodal techniques with transfer learning in deep neural networks to enhance the classification accuracy of cutaneous diseases.
{"title":"An Integrated Multimodal Deep Learning Framework for Accurate Skin Disease Classification","authors":"S. Hamida, Driss Lamrani, Mohammed Amine Bouqentar, Oussama El Gannour, B. Cherradi","doi":"10.3991/ijoe.v20i02.43795","DOIUrl":"https://doi.org/10.3991/ijoe.v20i02.43795","url":null,"abstract":"In order to effectively treat skin diseases, an accurate and prompt diagnosis is required. In this article, a novel method for classifying skin disorders using a multimodal classifier is presented. The proposed classifier utilizes multiple information sources to enhance the accuracy of disease classification. It incorporates images of skin lesions and patient-specific data. The multimodal classifier simultaneously classifies diseases by combining image and structured data inputs. The effectiveness of the proposed classifier was evaluated using the ISIC 2018 dataset, which includes images and clinical data for seven categories of skin diseases. The results indicate that the proposed model outperforms conventional single-modal and single-task classifiers, achieving an accuracy of 98.66% for image classification and 94.40% for clinical data classification. In addition, we compare the performance of the proposed model with that of other methodologies, demonstrating its superiority. Despite yielding promising results, the proposed method has limitations in terms of data requirements and generalizability. Future research directions include incorporating additional information sources, investigating genetic data integration, and applying the method to various medical conditions. This study illustrates the potential of integrating multimodal techniques with transfer learning in deep neural networks to enhance the classification accuracy of cutaneous diseases.","PeriodicalId":507997,"journal":{"name":"International Journal of Online and Biomedical Engineering (iJOE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139777163","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}
Pub Date : 2024-02-14DOI: 10.3991/ijoe.v20i02.45981
Mohammed Waly, Fahad Alshammari, Maryam E. Alshammari, Mohammed Algahtany
The subjective visual vertical (SVV) is a potential indicator of vestibular dysfunction as it assesses an individual’s perception of a vertical line. Despite this, and as a result of specific logistical impediments, SVV has not entered standard clinical practice. Dizziness is the third most common clinical complaint by patients (20%) in outpatient offices. It adversely affects the patient’s life and is often accompanied by intensive healthcare. This study aims to determine whether the bucket test and mobile phone app are as reliable as the Virtual SVV system in assessing the SVV. This study involves four types of investigation to determine the relationship or difference among three tests, including their performance comparison, descriptive analysis, one-way ANOVA test, receiver operating characteristic (ROC) curve, and correlation analysis. After organizing the raw data from 207 healthy volunteer participants for 8 trials, it was found that 59% were female and 41% were male. The data was analyzed utilizing the SPSS program. The test performance is measured using the ROC curve, and the results indicate that the bucket with the highest ROC coefficient is 0.72.
{"title":"Assessing Subjective Visual Vertical Reliability: A Comparison of the “Bucket Test,” a Mobile App, and a Virtual System","authors":"Mohammed Waly, Fahad Alshammari, Maryam E. Alshammari, Mohammed Algahtany","doi":"10.3991/ijoe.v20i02.45981","DOIUrl":"https://doi.org/10.3991/ijoe.v20i02.45981","url":null,"abstract":"The subjective visual vertical (SVV) is a potential indicator of vestibular dysfunction as it assesses an individual’s perception of a vertical line. Despite this, and as a result of specific logistical impediments, SVV has not entered standard clinical practice. Dizziness is the third most common clinical complaint by patients (20%) in outpatient offices. It adversely affects the patient’s life and is often accompanied by intensive healthcare. This study aims to determine whether the bucket test and mobile phone app are as reliable as the Virtual SVV system in assessing the SVV. This study involves four types of investigation to determine the relationship or difference among three tests, including their performance comparison, descriptive analysis, one-way ANOVA test, receiver operating characteristic (ROC) curve, and correlation analysis. After organizing the raw data from 207 healthy volunteer participants for 8 trials, it was found that 59% were female and 41% were male. The data was analyzed utilizing the SPSS program. The test performance is measured using the ROC curve, and the results indicate that the bucket with the highest ROC coefficient is 0.72.","PeriodicalId":507997,"journal":{"name":"International Journal of Online and Biomedical Engineering (iJOE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139779606","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}
Pub Date : 2024-02-14DOI: 10.3991/ijoe.v20i02.46789
Diego Alberto Cadillo-Laurentt, Ernesto Paiva-Peredo
Breast cancer is currently one of the most diagnosed oncological diseases worldwide, with thousands of new cases per year. Early detection and identifying its progression are key to overcoming the mortality rate. A recurrent test, to determine how far the disease has spread throughout the patient’s body, is the histological analysis of the sentinel lymph node near the breast. Although an expert pathologist performs this, it is usually an exhausting and time-consuming task, with a high possibility of error. This work presents a method to detect breast cancer metastasis through histological imaging of sentinel lymph nodes using convolutional neural networks. In this study, the performance of three models DenseNet-121, DenseNet-169 and DenseNet-201 are tested and compared. Experimental results indicated that the accuracy, precision, sensitivity and specificity (97.93%, 97.4%, 97.48% and 98.24%) of DenseNet-201 could reduce pathologist errors during the diagnostic process or serve as a second opinion tool.
{"title":"Histopathological Image Classification Using Convolutional Neural Networks for Detection of Metastatic Breast Cancer in Lymph Nodes","authors":"Diego Alberto Cadillo-Laurentt, Ernesto Paiva-Peredo","doi":"10.3991/ijoe.v20i02.46789","DOIUrl":"https://doi.org/10.3991/ijoe.v20i02.46789","url":null,"abstract":"Breast cancer is currently one of the most diagnosed oncological diseases worldwide, with thousands of new cases per year. Early detection and identifying its progression are key to overcoming the mortality rate. A recurrent test, to determine how far the disease has spread throughout the patient’s body, is the histological analysis of the sentinel lymph node near the breast. Although an expert pathologist performs this, it is usually an exhausting and time-consuming task, with a high possibility of error. This work presents a method to detect breast cancer metastasis through histological imaging of sentinel lymph nodes using convolutional neural networks. In this study, the performance of three models DenseNet-121, DenseNet-169 and DenseNet-201 are tested and compared. Experimental results indicated that the accuracy, precision, sensitivity and specificity (97.93%, 97.4%, 97.48% and 98.24%) of DenseNet-201 could reduce pathologist errors during the diagnostic process or serve as a second opinion tool.","PeriodicalId":507997,"journal":{"name":"International Journal of Online and Biomedical Engineering (iJOE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139837873","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}
Pub Date : 2024-02-14DOI: 10.3991/ijoe.v20i02.43099
Eduardo Arias Navarro, Cesar Nahuel Moya, Fiorella Lizano-Sánchez, Carlos Arguedas-Matarrita, César Eduardo Mora Ley, Ignacio Idoyaga
This article presents the results of the educational use of an ultra-concurrent laboratory during the second semester of 2022, in the Cisale Chair of the Common Cycle of the University of Buenos Aires in order to strengthen the experimental scenarios and quality of the process in the teaching of physics. For this purpose, a quantitative descriptive study in which 68 students participated was carried out. This allowed establishing a significant scenario with the implementation of the ultra-concurrent free-fall laboratory to enhance experimental development in physics teaching processes. It is concluded that remote laboratories are promising technologies for teaching physics at the university level. However, it should be clarified that the impact of an educational innovation does not only depend on the technology used, but also on the didactic design with which it is approached.
{"title":"Study of Free Fall Using an Ultra-Concurrent Laboratory at the University","authors":"Eduardo Arias Navarro, Cesar Nahuel Moya, Fiorella Lizano-Sánchez, Carlos Arguedas-Matarrita, César Eduardo Mora Ley, Ignacio Idoyaga","doi":"10.3991/ijoe.v20i02.43099","DOIUrl":"https://doi.org/10.3991/ijoe.v20i02.43099","url":null,"abstract":"This article presents the results of the educational use of an ultra-concurrent laboratory during the second semester of 2022, in the Cisale Chair of the Common Cycle of the University of Buenos Aires in order to strengthen the experimental scenarios and quality of the process in the teaching of physics. For this purpose, a quantitative descriptive study in which 68 students participated was carried out. This allowed establishing a significant scenario with the implementation of the ultra-concurrent free-fall laboratory to enhance experimental development in physics teaching processes. It is concluded that remote laboratories are promising technologies for teaching physics at the university level. However, it should be clarified that the impact of an educational innovation does not only depend on the technology used, but also on the didactic design with which it is approached.","PeriodicalId":507997,"journal":{"name":"International Journal of Online and Biomedical Engineering (iJOE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139778725","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}
Pub Date : 2024-02-14DOI: 10.3991/ijoe.v20i02.46817
José L. Serna-Landivar, Madelaine Violeta Risco Sernaqué, Ana Beatriz Rivas Moreano, William C. Algoner, Daniela M. Anticona-Valderrama, Walter Enrique Zúñiga Porras, Carlos Oliva Guevara
Crossed spherical gearing is used in the joints of robotic arm prostheses and allows mobility in 3 degrees of freedom. This paper aims to evaluate the design of a cross-spherical gear with three different materials, PEEK, AISI 304L, and Ti-6Al-4V, for a robotic arm prosthesis by finite element analysis. ANSYS mechanical software (version 2021 R1) was used to perform the static analysis and evaluate the deformations and stresses, modal analysis of natural frequencies and vibration modes, and high cycle fatigue analysis to determine fatigue resistance. The results obtained in the static analysis show that the maximum stresses are in the same zones for the three materials and have similar values. However, the Ti-6Al-4V and ASI 304L materials have a higher safety factor than PEEK, with a value of 5.17. In conclusion, the crossed spherical gearing is numerically validated using the finite element analysis so that the prototype can be later manufactured at an experimental level, and the values obtained for the crossed spherical gearing of the robotic arm prosthesis can be verified.
{"title":"Static, Dynamic, and High Cycle Fatigue Analysis of Crossed Spherical Gearing for Robotic Arm Ball Joint: A Finite Element Analysis Approach","authors":"José L. Serna-Landivar, Madelaine Violeta Risco Sernaqué, Ana Beatriz Rivas Moreano, William C. Algoner, Daniela M. Anticona-Valderrama, Walter Enrique Zúñiga Porras, Carlos Oliva Guevara","doi":"10.3991/ijoe.v20i02.46817","DOIUrl":"https://doi.org/10.3991/ijoe.v20i02.46817","url":null,"abstract":"Crossed spherical gearing is used in the joints of robotic arm prostheses and allows mobility in 3 degrees of freedom. This paper aims to evaluate the design of a cross-spherical gear with three different materials, PEEK, AISI 304L, and Ti-6Al-4V, for a robotic arm prosthesis by finite element analysis. ANSYS mechanical software (version 2021 R1) was used to perform the static analysis and evaluate the deformations and stresses, modal analysis of natural frequencies and vibration modes, and high cycle fatigue analysis to determine fatigue resistance. The results obtained in the static analysis show that the maximum stresses are in the same zones for the three materials and have similar values. However, the Ti-6Al-4V and ASI 304L materials have a higher safety factor than PEEK, with a value of 5.17. In conclusion, the crossed spherical gearing is numerically validated using the finite element analysis so that the prototype can be later manufactured at an experimental level, and the values obtained for the crossed spherical gearing of the robotic arm prosthesis can be verified.","PeriodicalId":507997,"journal":{"name":"International Journal of Online and Biomedical Engineering (iJOE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139838374","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}
This study investigates the implementation, effectiveness, and impact of a unique e-learning system designed specifically for emergency signs and cardiopulmonary resuscitation (CPR) emergency preparedness in marathon events. Our approach introduces the first e-learning system specifically designed for marathon events. It delivers engaging content, including infographic stories, expert lectures, and interactive modules, to provide registered runners with comprehensive knowledge of first aid and emergency signs for CPR. To evaluate the e-learning application, we conducted a comparative experiment during the CMU (Chiang Mai University) marathon with 9,761 participants. We used pre- and post-tests, as well as a survey questionnaire. The results showed significant improvements in participants’ CPR knowledge across all educational backgrounds. The integration of e-learning into the registration process contributed to a safer marathon environment, as participants felt more confident in handling emergencies. Approximately 85% of participants expressed a willingness to recommend the e-learning system. This increased confidence among participants in handling emergencies benefits both runners and marathon organizers by enhancing safety measures and emergency response during events. In conclusion, our findings strongly support the integration of e-learning into the registration process for marathon events. Recommendations based on our research include providing comprehensive guidelines for other marathon events, instilling stakeholder confidence, and emphasizing the suitability of e-learning for medium- to largescale events. However, caution is advised for smaller events due to potential complexities and costs. Additionally, we suggest limiting the validity of e-certificates to ensure that participants have up-to-date CPR knowledge.
{"title":"Effectiveness of an E-learning System for Emergency Signs and CPR Emergency Preparedness in Marathon Events: A Comparative Study","authors":"Pipitton Homla, Pakinee Ariya, Perasuk Worragin, Supicha Niemsup, Kitti Puritat, K. Intawong","doi":"10.3991/ijoe.v20i01.44927","DOIUrl":"https://doi.org/10.3991/ijoe.v20i01.44927","url":null,"abstract":"This study investigates the implementation, effectiveness, and impact of a unique e-learning system designed specifically for emergency signs and cardiopulmonary resuscitation (CPR) emergency preparedness in marathon events. Our approach introduces the first e-learning system specifically designed for marathon events. It delivers engaging content, including infographic stories, expert lectures, and interactive modules, to provide registered runners with comprehensive knowledge of first aid and emergency signs for CPR. To evaluate the e-learning application, we conducted a comparative experiment during the CMU (Chiang Mai University) marathon with 9,761 participants. We used pre- and post-tests, as well as a survey questionnaire. The results showed significant improvements in participants’ CPR knowledge across all educational backgrounds. The integration of e-learning into the registration process contributed to a safer marathon environment, as participants felt more confident in handling emergencies. Approximately 85% of participants expressed a willingness to recommend the e-learning system. This increased confidence among participants in handling emergencies benefits both runners and marathon organizers by enhancing safety measures and emergency response during events. In conclusion, our findings strongly support the integration of e-learning into the registration process for marathon events. Recommendations based on our research include providing comprehensive guidelines for other marathon events, instilling stakeholder confidence, and emphasizing the suitability of e-learning for medium- to largescale events. However, caution is advised for smaller events due to potential complexities and costs. Additionally, we suggest limiting the validity of e-certificates to ensure that participants have up-to-date CPR knowledge.","PeriodicalId":507997,"journal":{"name":"International Journal of Online and Biomedical Engineering (iJOE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139624140","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}
Pub Date : 2024-01-12DOI: 10.3991/ijoe.v20i01.41363
N. H. Ja'afar, Afandi Ahmad, S. Safie
Medical imaging plays a significant role in clinical practice. Storing and transferring a large volume of images can be complex and inefficient. This paper presents the development of a new compression technique that combines the fast discrete curvelet transform (FDCvT) with state table set partitioning in the hierarchical trees (STS) encoding scheme. The curvelet transform is an extension of the wavelet transform algorithm that represents data based on scale and position. Initially, the medical image was decomposed using the FDCvT algorithm. The FDCvT algorithm creates symmetrical values for the detail coefficients, and these coefficients are modified to improve the efficiency of the algorithm. The curvelet coefficients are then encoded using the STS and differential pulse-code modulation (DPCM). The greatest amount of energy is contained in the coarse coefficients, which are encoded using the DPCM method. The finest and modified detail coefficients are encoded using the STS method. A variety of medical modalities, including computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI), are used to verify the performance of the proposed technique. Various quality metrics, including peak signal-to-noise ratio (PSNR), compression ratio (CR), and structural similarity index (SSIM), are used to evaluate the compression results. Additionally, the computation time for the encoding (ET) and decoding (DT) processes is measured. The experimental results showed that the PET image obtained higher values of the PSNR and CR. The CT image provides high quality for the reconstructed image, with an SSIM value of 0.96 and the fastest ET of 0.13 seconds. The MRI image has the shortest DT, which is 0.23 seconds.
{"title":"A State Table SPHIT Approach for Modified Curvelet-based Medical Image Compression","authors":"N. H. Ja'afar, Afandi Ahmad, S. Safie","doi":"10.3991/ijoe.v20i01.41363","DOIUrl":"https://doi.org/10.3991/ijoe.v20i01.41363","url":null,"abstract":"Medical imaging plays a significant role in clinical practice. Storing and transferring a large volume of images can be complex and inefficient. This paper presents the development of a new compression technique that combines the fast discrete curvelet transform (FDCvT) with state table set partitioning in the hierarchical trees (STS) encoding scheme. The curvelet transform is an extension of the wavelet transform algorithm that represents data based on scale and position. Initially, the medical image was decomposed using the FDCvT algorithm. The FDCvT algorithm creates symmetrical values for the detail coefficients, and these coefficients are modified to improve the efficiency of the algorithm. The curvelet coefficients are then encoded using the STS and differential pulse-code modulation (DPCM). The greatest amount of energy is contained in the coarse coefficients, which are encoded using the DPCM method. The finest and modified detail coefficients are encoded using the STS method. A variety of medical modalities, including computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI), are used to verify the performance of the proposed technique. Various quality metrics, including peak signal-to-noise ratio (PSNR), compression ratio (CR), and structural similarity index (SSIM), are used to evaluate the compression results. Additionally, the computation time for the encoding (ET) and decoding (DT) processes is measured. The experimental results showed that the PET image obtained higher values of the PSNR and CR. The CT image provides high quality for the reconstructed image, with an SSIM value of 0.96 and the fastest ET of 0.13 seconds. The MRI image has the shortest DT, which is 0.23 seconds.","PeriodicalId":507997,"journal":{"name":"International Journal of Online and Biomedical Engineering (iJOE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139624224","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}
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