Pub Date : 2023-09-18DOI: 10.3991/ijoe.v19i13.41219
Doni Tri Putra Yanto, None Ganefri, None Hastuti, Oriza Candra, Maryatun Kabatiah, None Andrian, Hermi Zaswita
Virtual laboratory (VL) has become increasingly popular in Post-COVID-19 to support practical learning in the remote learning system. The use of VL was responded to by students with different attitudes. This study discusses the factors that influence the perception of Industrial Electrical Engineering (IEE) students in responding to the use of the VL in the learning process of the Electrical Machines Practicum Course. Based on the technology acceptance model (TAM), students’ attitudes toward using VL (are influenced by perceived ease of use (PEU) and perceived usefulness (PU). At the same time, PU also acts as an intervening variable. The research involved IEE students of the Electrical Engineering Department, at Universitas Negeri Padang. Data collection was carried out by survey using a questionnaire. Quantitative data were analyzed using variant-based structural equation modelling (SEM), with partial least square (PLS) or PLS-SEM. The results showed a significant positive effect between PEU and PU from the VL used against A. PU’s role as an intervener was also positive in mediating the effect of PEU on A so it became more prominent. Thus, it can be concluded that PEU and PU are the factors that must be considered in choosing VL to be applied to a practical learning process in the remote learning system.
{"title":"The Affecting Factors of Students' Attitudes Toward the Use of a Virtual Laboratory: A Study in Industrial Electrical Engineering","authors":"Doni Tri Putra Yanto, None Ganefri, None Hastuti, Oriza Candra, Maryatun Kabatiah, None Andrian, Hermi Zaswita","doi":"10.3991/ijoe.v19i13.41219","DOIUrl":"https://doi.org/10.3991/ijoe.v19i13.41219","url":null,"abstract":"Virtual laboratory (VL) has become increasingly popular in Post-COVID-19 to support practical learning in the remote learning system. The use of VL was responded to by students with different attitudes. This study discusses the factors that influence the perception of Industrial Electrical Engineering (IEE) students in responding to the use of the VL in the learning process of the Electrical Machines Practicum Course. Based on the technology acceptance model (TAM), students’ attitudes toward using VL (are influenced by perceived ease of use (PEU) and perceived usefulness (PU). At the same time, PU also acts as an intervening variable. The research involved IEE students of the Electrical Engineering Department, at Universitas Negeri Padang. Data collection was carried out by survey using a questionnaire. Quantitative data were analyzed using variant-based structural equation modelling (SEM), with partial least square (PLS) or PLS-SEM. The results showed a significant positive effect between PEU and PU from the VL used against A. PU’s role as an intervener was also positive in mediating the effect of PEU on A so it became more prominent. Thus, it can be concluded that PEU and PU are the factors that must be considered in choosing VL to be applied to a practical learning process in the remote learning system.","PeriodicalId":36900,"journal":{"name":"International Journal of Online and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135151915","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 : 2023-09-18DOI: 10.3991/ijoe.v19i13.40091
Hasanin Mohammed Salman, Firas Husham Almukhtar
Recently, tablet-based devices have become significantly more utilized platforms for electronic medical record (EMR) systems. EMR is the digital counterpart of the medical doctor’s office paper charts. EMR systems contain the medical and treatment histories of the patients in a unified practice. Nevertheless, statistics indicate that a considerable percentage of medical doctors are elderly, aged 60 and above. As using mobile handheld devices (including tablets) poses a well-recognized usability challenge for elderly users, the user interface (UI) usability of tablet-based EMR systems must be thoroughly assessed, considering the needs of elderly medical doctors. Accordingly, our objective is to address this need. Three expert evaluators implemented the heuristic evaluation (HE) approach to evaluate the UI usability of a commercial EMR system that is a tablet-based platform. Applying the HE approach helped identify usability problems that elderly medical doctors might encounter when utilizing a tablet-based EMR UI. In total, eight usability problems contributed to the seven heuristic violations discovered.
{"title":"Usability Evaluation of Tablet-Based Electronic Medical Record Interface in Supporting Elderly Medical Doctors","authors":"Hasanin Mohammed Salman, Firas Husham Almukhtar","doi":"10.3991/ijoe.v19i13.40091","DOIUrl":"https://doi.org/10.3991/ijoe.v19i13.40091","url":null,"abstract":"Recently, tablet-based devices have become significantly more utilized platforms for electronic medical record (EMR) systems. EMR is the digital counterpart of the medical doctor’s office paper charts. EMR systems contain the medical and treatment histories of the patients in a unified practice. Nevertheless, statistics indicate that a considerable percentage of medical doctors are elderly, aged 60 and above. As using mobile handheld devices (including tablets) poses a well-recognized usability challenge for elderly users, the user interface (UI) usability of tablet-based EMR systems must be thoroughly assessed, considering the needs of elderly medical doctors. Accordingly, our objective is to address this need. Three expert evaluators implemented the heuristic evaluation (HE) approach to evaluate the UI usability of a commercial EMR system that is a tablet-based platform. Applying the HE approach helped identify usability problems that elderly medical doctors might encounter when utilizing a tablet-based EMR UI. In total, eight usability problems contributed to the seven heuristic violations discovered.","PeriodicalId":36900,"journal":{"name":"International Journal of Online and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135152316","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 : 2023-09-18DOI: 10.3991/ijoe.v19i13.42165
Mahasak Ketcham, Thittaporn Ganokratanaa
This research endeavor revolved around the conceptualization, design, and evaluation of a technologically advanced smart glove tailored to aid individuals afflicted with visual impairments. The focal objective entailed integrating a diverse array of sensors and sophisticated features within the glove’s framework to effectively detect barriers and offer location-specific assistance. Rigorous experimentation and analysis were conducted to meticulously scrutinize the device’s performance and ascertain its efficacy. The experimental findings unequivocally substantiated the glove’s competence in identifying obstacles obstructing the user’s path, with a striking accuracy rate exceeding 95%. Notably, when the user engaged the yellow button while concurrently gesturing forward, the glove adeptly detected impediments situated within a onefoot proximity, promptly generating an auditory alert through an embedded speaker module. Similarly, activating the red button triggered the activation of the GPS sensor, enabling real-time determination of the user’s precise geographical coordinates. Subsequently, this invaluable location data was expeditiously disseminated via Line Notify, accompanied by a conveniently accessible Google Maps hyperlink. Moreover, the aforementioned coordinates were seamlessly displayed on an interconnected web server, thus facilitating immediate assistance from nearby individuals. In essence, the culmination of this research effort showcased the immense potential of the developed intelligent glove as a viable tool for ameliorating the challenges faced by individuals confronting visual impairments. The comprehensive evaluation outcomes provide a solid foundation for future enhancements and refinements aimed at elevating the device’s functionality and user experience to unprecedented heights.
{"title":"Intelligent Gloves for Assisting Individuals with Visual Impairment","authors":"Mahasak Ketcham, Thittaporn Ganokratanaa","doi":"10.3991/ijoe.v19i13.42165","DOIUrl":"https://doi.org/10.3991/ijoe.v19i13.42165","url":null,"abstract":"This research endeavor revolved around the conceptualization, design, and evaluation of a technologically advanced smart glove tailored to aid individuals afflicted with visual impairments. The focal objective entailed integrating a diverse array of sensors and sophisticated features within the glove’s framework to effectively detect barriers and offer location-specific assistance. Rigorous experimentation and analysis were conducted to meticulously scrutinize the device’s performance and ascertain its efficacy. The experimental findings unequivocally substantiated the glove’s competence in identifying obstacles obstructing the user’s path, with a striking accuracy rate exceeding 95%. Notably, when the user engaged the yellow button while concurrently gesturing forward, the glove adeptly detected impediments situated within a onefoot proximity, promptly generating an auditory alert through an embedded speaker module. Similarly, activating the red button triggered the activation of the GPS sensor, enabling real-time determination of the user’s precise geographical coordinates. Subsequently, this invaluable location data was expeditiously disseminated via Line Notify, accompanied by a conveniently accessible Google Maps hyperlink. Moreover, the aforementioned coordinates were seamlessly displayed on an interconnected web server, thus facilitating immediate assistance from nearby individuals. In essence, the culmination of this research effort showcased the immense potential of the developed intelligent glove as a viable tool for ameliorating the challenges faced by individuals confronting visual impairments. The comprehensive evaluation outcomes provide a solid foundation for future enhancements and refinements aimed at elevating the device’s functionality and user experience to unprecedented heights.","PeriodicalId":36900,"journal":{"name":"International Journal of Online and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135151924","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 : 2023-09-18DOI: 10.3991/ijoe.v19i13.36185
Mohammed Amine Lafraxo, Hinde Hami, Tarik Merrakchi, Ali Azghar, Ahmed Remaida, Mohammed Ouadoud, Adil Maleb, Abdelmajid Soulaymani
This study aimed to build a recommender system that predicts the shape of bacteria for biological requests of urine cytobacteriological examination (UCBE) using machine learning techniques, to reduce the time taken to identify the shape of bacteria (Cocci or Bacilli). We used different methods and techniques in the process: Unified Modelling Language (UML) was used for digital design architecture, Rstudio tool with R programming language for system development, and Random Forest (RF) algorithm for the prediction. Experimental results showed that the time needed to identify the shape of bacteria is decreased, and bacilli bacteria are better recognized by the algorithm with an error rate of 3%. In addition to that, the proposed recommender system allows biologists to validate and correct the prediction and improve the accuracy of the classification algorithm used in the future.
{"title":"Building a Recommender System to Predict the Shape of Bacteria in Urine Cytobacteriological Examination Using Machine Learning","authors":"Mohammed Amine Lafraxo, Hinde Hami, Tarik Merrakchi, Ali Azghar, Ahmed Remaida, Mohammed Ouadoud, Adil Maleb, Abdelmajid Soulaymani","doi":"10.3991/ijoe.v19i13.36185","DOIUrl":"https://doi.org/10.3991/ijoe.v19i13.36185","url":null,"abstract":"This study aimed to build a recommender system that predicts the shape of bacteria for biological requests of urine cytobacteriological examination (UCBE) using machine learning techniques, to reduce the time taken to identify the shape of bacteria (Cocci or Bacilli). We used different methods and techniques in the process: Unified Modelling Language (UML) was used for digital design architecture, Rstudio tool with R programming language for system development, and Random Forest (RF) algorithm for the prediction. Experimental results showed that the time needed to identify the shape of bacteria is decreased, and bacilli bacteria are better recognized by the algorithm with an error rate of 3%. In addition to that, the proposed recommender system allows biologists to validate and correct the prediction and improve the accuracy of the classification algorithm used in the future.","PeriodicalId":36900,"journal":{"name":"International Journal of Online and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135152310","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 : 2023-09-18DOI: 10.3991/ijoe.v19i13.41431
Raul Jaúregui-Velarde, Pedro Molina-Velarde, Cesar Yactayo-Arias, Laberiano Andrade-Arenas
The Aedes aegypti mosquito transmits the dengue, zika, and chikungunya viruses, endangering the health and lives of people in affected countries due to a lack of timely diagnosis. The objective of this study is to design and evaluate the feasibility of a mobile application based on an expert system for early diagnosis of diseases transmitted by the Aedes aegypti mosquito. The Buchanan methodology was used to develop the application. The results obtained show that the proposed mobile application has a diagnostic accuracy of 83%, a sensitivity of 91%, a specificity of 63%, and an error rate of 17%. The technical aspects of the application were also evaluated through a questionnaire administered to five computer experts. The results showed that the technical aspects of the application received an average rating of 3.91 out of a maximum of 5, with a standard deviation of 0.482. In addition, the usability of the application was evaluated using the standardized System Usability Scale (SUS), which was administered to a total of 15 users. The results of this evaluation showed that the application received an average score of 83 on the SUS scale, indicating a positive level of usability. In conclusion, the results support the effectiveness and potential of the application for the early diagnosis of diseases transmitted by the Aedes aegypti mosquito, providing a useful tool for the rapid detection of these diseases. Although it requires more attention to specificity and error rate to improve the accuracy of the diagnosis.
{"title":"Evaluation of a Prototype Mobile Application Based on an Expert System for the Diagnosis of Diseases Transmitted by the Aedes Aegypti Mosquito","authors":"Raul Jaúregui-Velarde, Pedro Molina-Velarde, Cesar Yactayo-Arias, Laberiano Andrade-Arenas","doi":"10.3991/ijoe.v19i13.41431","DOIUrl":"https://doi.org/10.3991/ijoe.v19i13.41431","url":null,"abstract":"The Aedes aegypti mosquito transmits the dengue, zika, and chikungunya viruses, endangering the health and lives of people in affected countries due to a lack of timely diagnosis. The objective of this study is to design and evaluate the feasibility of a mobile application based on an expert system for early diagnosis of diseases transmitted by the Aedes aegypti mosquito. The Buchanan methodology was used to develop the application. The results obtained show that the proposed mobile application has a diagnostic accuracy of 83%, a sensitivity of 91%, a specificity of 63%, and an error rate of 17%. The technical aspects of the application were also evaluated through a questionnaire administered to five computer experts. The results showed that the technical aspects of the application received an average rating of 3.91 out of a maximum of 5, with a standard deviation of 0.482. In addition, the usability of the application was evaluated using the standardized System Usability Scale (SUS), which was administered to a total of 15 users. The results of this evaluation showed that the application received an average score of 83 on the SUS scale, indicating a positive level of usability. In conclusion, the results support the effectiveness and potential of the application for the early diagnosis of diseases transmitted by the Aedes aegypti mosquito, providing a useful tool for the rapid detection of these diseases. Although it requires more attention to specificity and error rate to improve the accuracy of the diagnosis.","PeriodicalId":36900,"journal":{"name":"International Journal of Online and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135152317","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 : 2023-09-18DOI: 10.3991/ijoe.v19i13.41871
Soufiane Ardchir, Youssef Ouassit, Soumaya Ounacer, Mohammed Yassine EL Ghoumari, Mohamed Azzouazi
The liver disease has become a pressing global issue, with a sharp increase in cases reported worldwide. Detecting liver disease can be difficult as it often has few noticeable symptoms, which means that by the time it is detected, it may have already progressed to an advanced stage, resulting in many people dying without even realizing they had it. Early detection is crucial as it enables patients to begin treatment earlier, which can potentially save their lives. This study aimed to assess the efficacy of five ensemble machine learning (ML) models, namely RF, XGBoost, Extra Trees, bagging, and stacking methods, in predicting liver disease. It uses the ILPD dataset. To prevent overfitting and biases in the dataset, several pre-processing statistical techniques were employed to handle missing data, outliers, and data balancing. The study’s results underline the importance of using the RFE feature selection method, which allowed the use of only the most relevant features for the model, which may have improved the accuracy and efficiency of the model. The study found that the highest testing accuracy of 93% was achieved by the proposed model, which utilized an improved preprocessing approach and a stacking ensemble classifier with RFE feature selection. The use of ensemble ML has given promising results. Indeed, medical professionals can develop models better equipped to handle the complexity and variability of medical data, resulting in more accurate diagnoses, more effective treatment plans, and better patient outcomes.
{"title":"Integrated Ensemble Learning Framework for Predicting Liver Disease","authors":"Soufiane Ardchir, Youssef Ouassit, Soumaya Ounacer, Mohammed Yassine EL Ghoumari, Mohamed Azzouazi","doi":"10.3991/ijoe.v19i13.41871","DOIUrl":"https://doi.org/10.3991/ijoe.v19i13.41871","url":null,"abstract":"The liver disease has become a pressing global issue, with a sharp increase in cases reported worldwide. Detecting liver disease can be difficult as it often has few noticeable symptoms, which means that by the time it is detected, it may have already progressed to an advanced stage, resulting in many people dying without even realizing they had it. Early detection is crucial as it enables patients to begin treatment earlier, which can potentially save their lives. This study aimed to assess the efficacy of five ensemble machine learning (ML) models, namely RF, XGBoost, Extra Trees, bagging, and stacking methods, in predicting liver disease. It uses the ILPD dataset. To prevent overfitting and biases in the dataset, several pre-processing statistical techniques were employed to handle missing data, outliers, and data balancing. The study’s results underline the importance of using the RFE feature selection method, which allowed the use of only the most relevant features for the model, which may have improved the accuracy and efficiency of the model. The study found that the highest testing accuracy of 93% was achieved by the proposed model, which utilized an improved preprocessing approach and a stacking ensemble classifier with RFE feature selection. The use of ensemble ML has given promising results. Indeed, medical professionals can develop models better equipped to handle the complexity and variability of medical data, resulting in more accurate diagnoses, more effective treatment plans, and better patient outcomes.","PeriodicalId":36900,"journal":{"name":"International Journal of Online and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135152321","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 : 2023-09-18DOI: 10.3991/ijoe.v19i13.40145
Peddarapu Ramakrishna, Pothuraju Rajarajeswari
In the field of bioinformatics, a vast amount of biological data has been generated thanks to the digitalization of high-throughput devices at a reduced cost. Managing such large datasets has become a challenging task for identifying disease-causing genes. Microarray technology enables the simultaneous monitoring of gene expression levels, thereby improving disease diagnosis accuracy for conditions like diabetes, hepatitis, and cancer. As these complex datasets become more accessible, innovative data analytics approaches are necessary to extract meaningful knowledge. Machine learning and data mining techniques can be employed to leverage big and heterogeneous data sources, facilitating biomedical research and healthcare delivery. Data mining has emerged as a vital tool in the medical field, providing insights into illnesses and treatments and enhancing the efficiency of healthcare systems. This thesis aims to present a novel hybrid technique for feature selection using amalgamation wrappers. The proposed approach combines the Mayfly and whale survival strategies, leveraging the strengths of both algorithms. The model was evaluated using various datasets and assessment criteria, including precision, accuracy, recall, F1-score, and specificity. The simulation results demonstrated that the proposed integrated optimization model exhibits improved classification performance with 12% higher accuracy in disease diagnosis.
{"title":"Evolutionary Optimization Algorithm for Classification of Microarray Datasets with Mayfly and Whale Survival","authors":"Peddarapu Ramakrishna, Pothuraju Rajarajeswari","doi":"10.3991/ijoe.v19i13.40145","DOIUrl":"https://doi.org/10.3991/ijoe.v19i13.40145","url":null,"abstract":"In the field of bioinformatics, a vast amount of biological data has been generated thanks to the digitalization of high-throughput devices at a reduced cost. Managing such large datasets has become a challenging task for identifying disease-causing genes. Microarray technology enables the simultaneous monitoring of gene expression levels, thereby improving disease diagnosis accuracy for conditions like diabetes, hepatitis, and cancer. As these complex datasets become more accessible, innovative data analytics approaches are necessary to extract meaningful knowledge. Machine learning and data mining techniques can be employed to leverage big and heterogeneous data sources, facilitating biomedical research and healthcare delivery. Data mining has emerged as a vital tool in the medical field, providing insights into illnesses and treatments and enhancing the efficiency of healthcare systems. This thesis aims to present a novel hybrid technique for feature selection using amalgamation wrappers. The proposed approach combines the Mayfly and whale survival strategies, leveraging the strengths of both algorithms. The model was evaluated using various datasets and assessment criteria, including precision, accuracy, recall, F1-score, and specificity. The simulation results demonstrated that the proposed integrated optimization model exhibits improved classification performance with 12% higher accuracy in disease diagnosis.","PeriodicalId":36900,"journal":{"name":"International Journal of Online and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135151925","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 : 2023-09-18DOI: 10.3991/ijoe.v19i13.40897
José Ignacio Guzmán, Mauricio Herrera, Camilo Rodríguez Beltrán
This study presents a new minimal access surgery training system, SECMA, and its constructive validation to determine its usefulness for training basic laparoscopic skills. SECMA is an affordable, highly portable, mobile virtual reality training tool for laparoscopic techniques that integrates the Oculus Quest with a mechanical interface for surgeon simulation of forceps using the hand controllers of these devices. It allows the execution of structured activities (supported by virtual scenarios simulating operating rooms developed in Unity), performance evaluation, and real-time data capture. Two experiments were carried out: 1) coordination; and 2) capture and transport, with a total of 21 individuals divided into two groups: a novice group (inexperienced) of 10 participants and an expert group (>100 endoscopic procedures) of 11 participants. Total task time score, right-hand speed, path length, and other metrics from several consecutive runs on the simulator were compared between experts and novices. Data automatically recorded by SECMA during the experiments were analyzed using hypothesis tests, linear regressions, analysis of variance, principal component analysis, and machine learning-supervised classifiers. In the experiments, the experts scored significantly better than the novices in all the parameters used. The tasks evaluated discriminated between the skills of experienced and novice surgeons, giving the first indication of construct validity for SECMA.
{"title":"New Highly Portable Simulator (SECMA) Based on Virtual Reality for Teaching Essential Skills in Minimally Invasive Surgeries","authors":"José Ignacio Guzmán, Mauricio Herrera, Camilo Rodríguez Beltrán","doi":"10.3991/ijoe.v19i13.40897","DOIUrl":"https://doi.org/10.3991/ijoe.v19i13.40897","url":null,"abstract":"This study presents a new minimal access surgery training system, SECMA, and its constructive validation to determine its usefulness for training basic laparoscopic skills. SECMA is an affordable, highly portable, mobile virtual reality training tool for laparoscopic techniques that integrates the Oculus Quest with a mechanical interface for surgeon simulation of forceps using the hand controllers of these devices. It allows the execution of structured activities (supported by virtual scenarios simulating operating rooms developed in Unity), performance evaluation, and real-time data capture. Two experiments were carried out: 1) coordination; and 2) capture and transport, with a total of 21 individuals divided into two groups: a novice group (inexperienced) of 10 participants and an expert group (>100 endoscopic procedures) of 11 participants. Total task time score, right-hand speed, path length, and other metrics from several consecutive runs on the simulator were compared between experts and novices. Data automatically recorded by SECMA during the experiments were analyzed using hypothesis tests, linear regressions, analysis of variance, principal component analysis, and machine learning-supervised classifiers. In the experiments, the experts scored significantly better than the novices in all the parameters used. The tasks evaluated discriminated between the skills of experienced and novice surgeons, giving the first indication of construct validity for SECMA.","PeriodicalId":36900,"journal":{"name":"International Journal of Online and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135151921","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 : 2023-09-18DOI: 10.3991/ijoe.v19i13.40523
Dipanjan Acharya, K Eashwer, Soumya Kumar, R Sivakumar, P C Kishoreraja, None Ramasamy Srinivasagan
The COVID-19 disease outbreak resulted in a worldwide pandemic. Currently, the reverse transcription-polymerase chain reaction (RT-PCR), which relies on nasopharyngeal swabs to examine the existence of the ribonucleic acid (RNA) of SARS-CoV-27, is still a popular approach to testing for the disease. Despite the high level of specificity of testing with RT-PCR, the sensitivity of the method could be relatively low, and there is significant variability in efficacy depending on different sampling methods and the time of occurrence of symptoms. It is therefore essential for us to develop a machine-learning algorithm that can analyze computerized tomography images to detect the presence of COVID-19. Besides COVID-19, lung computerized tomography (CT) scan images can detect many other diseases, such as lung cancer, pneumonia, etc. This paper deals with the implementation of an algorithm that takes lung CT scans and lung X-ray images as input and predicts a list of probable diseases and possible diagnoses that infect the lungs. Machine learning algorithms will be able to predict disease by scanning the tiniest of regions easily missed by the human eye. This paper presents a survey of various machine learning algorithms that aid in detecting multiple diseases in lung CT scan images. Apart from the study of standard algorithms best suited for COVID-19 detection, this paper also includes recent trends. One of the major recent trends that can be incorporated into COVID-19 detection is TinyML. Tiny ML is an emerging area in machine learning algorithms that can be used to detect multiple diseases in lung CT scan images with better accuracy and in less time. This tool can aid doctors in their diagnosis and treatment of patients and help increase the efficiency of the treatment process. While understanding the features and mapping them using a hidden layer, there is a probability of compressing the dataset, as well as the model to process and classify the low-bit images in real-time using TinyML.
{"title":"Multiple Disease Detection using Machine Learning Techniques","authors":"Dipanjan Acharya, K Eashwer, Soumya Kumar, R Sivakumar, P C Kishoreraja, None Ramasamy Srinivasagan","doi":"10.3991/ijoe.v19i13.40523","DOIUrl":"https://doi.org/10.3991/ijoe.v19i13.40523","url":null,"abstract":"The COVID-19 disease outbreak resulted in a worldwide pandemic. Currently, the reverse transcription-polymerase chain reaction (RT-PCR), which relies on nasopharyngeal swabs to examine the existence of the ribonucleic acid (RNA) of SARS-CoV-27, is still a popular approach to testing for the disease. Despite the high level of specificity of testing with RT-PCR, the sensitivity of the method could be relatively low, and there is significant variability in efficacy depending on different sampling methods and the time of occurrence of symptoms. It is therefore essential for us to develop a machine-learning algorithm that can analyze computerized tomography images to detect the presence of COVID-19. Besides COVID-19, lung computerized tomography (CT) scan images can detect many other diseases, such as lung cancer, pneumonia, etc. This paper deals with the implementation of an algorithm that takes lung CT scans and lung X-ray images as input and predicts a list of probable diseases and possible diagnoses that infect the lungs. Machine learning algorithms will be able to predict disease by scanning the tiniest of regions easily missed by the human eye. This paper presents a survey of various machine learning algorithms that aid in detecting multiple diseases in lung CT scan images. Apart from the study of standard algorithms best suited for COVID-19 detection, this paper also includes recent trends. One of the major recent trends that can be incorporated into COVID-19 detection is TinyML. Tiny ML is an emerging area in machine learning algorithms that can be used to detect multiple diseases in lung CT scan images with better accuracy and in less time. This tool can aid doctors in their diagnosis and treatment of patients and help increase the efficiency of the treatment process. While understanding the features and mapping them using a hidden layer, there is a probability of compressing the dataset, as well as the model to process and classify the low-bit images in real-time using TinyML.","PeriodicalId":36900,"journal":{"name":"International Journal of Online and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135151926","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 : 2023-09-18DOI: 10.3991/ijoe.v19i13.41197
Deepali Newaskar, B.P. Patil
Active Implantable Medical Devices (AIMDs) act as lifesaving devices. They provide electrical signals to tissues as well as perform data-logging operations. To perform these operations, they need power. The battery is the only source for such devices, as they are placed invasively inside the human body. Once the battery drains out, the patient wearing the device has to undergo medical surgery for the second time, where there are many chances of infections, and it could be life-threatening too. If the AIMDs, e.g., pacemakers are designed using rechargeable batteries, then the devices can be recharged regularly, which can increase the life of the device as well as reduce its size. Wireless charging of AIMDs such as ICDs or pacemakers is proposed in this paper using magnetic resonant coupling. The selection of frequency for power transfer is the most crucial part, as the basic restriction (BR) criteria proposed by ICNIRP guidelines and the IEEEC95.1 standard need to be followed, which ensures the safety of the patient. This is suggested by considering some basic restriction parameters, such as specific absorption rate (SAR) and current density, as suggested by guidelines. In this paper, experimentation using two frequencies is shown, i.e., 1.47 MHz (the high frequency) and 62 KHz (the low frequency). For experimentation, goat flesh and saline solution are used. Secondary coil and flesh are dipped in the saline solution. Battery recharging performed at a lower frequency took less time than with a frequency in the MHz range. All BR criteria are fulfilled for both frequencies, so the proposed methodology is safe to use.
{"title":"Rechargeable Active Implantable Medical Devices (AIMDs)","authors":"Deepali Newaskar, B.P. Patil","doi":"10.3991/ijoe.v19i13.41197","DOIUrl":"https://doi.org/10.3991/ijoe.v19i13.41197","url":null,"abstract":"Active Implantable Medical Devices (AIMDs) act as lifesaving devices. They provide electrical signals to tissues as well as perform data-logging operations. To perform these operations, they need power. The battery is the only source for such devices, as they are placed invasively inside the human body. Once the battery drains out, the patient wearing the device has to undergo medical surgery for the second time, where there are many chances of infections, and it could be life-threatening too. If the AIMDs, e.g., pacemakers are designed using rechargeable batteries, then the devices can be recharged regularly, which can increase the life of the device as well as reduce its size. Wireless charging of AIMDs such as ICDs or pacemakers is proposed in this paper using magnetic resonant coupling. The selection of frequency for power transfer is the most crucial part, as the basic restriction (BR) criteria proposed by ICNIRP guidelines and the IEEEC95.1 standard need to be followed, which ensures the safety of the patient. This is suggested by considering some basic restriction parameters, such as specific absorption rate (SAR) and current density, as suggested by guidelines. In this paper, experimentation using two frequencies is shown, i.e., 1.47 MHz (the high frequency) and 62 KHz (the low frequency). For experimentation, goat flesh and saline solution are used. Secondary coil and flesh are dipped in the saline solution. Battery recharging performed at a lower frequency took less time than with a frequency in the MHz range. All BR criteria are fulfilled for both frequencies, so the proposed methodology is safe to use.","PeriodicalId":36900,"journal":{"name":"International Journal of Online and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135152312","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：