Presents corrections to the paper, Gastric Section Correlation Network for Gastric Precancerous Lesion Diagnosis.
介绍对论文《用于胃癌前病变诊断的胃部切片相关网络》的更正。
{"title":"Corrections to “Gastric Section Correlation Network for Gastric Precancerous Lesion Diagnosis”","authors":"Jyun-Yao Jhang;Yu-Ching Tsai;Tzu-Chun Hsu;Chun-Rong Huang;Hsiu-Chi Cheng;Bor-Shyang Sheu","doi":"10.1109/OJEMB.2024.3452970","DOIUrl":"https://doi.org/10.1109/OJEMB.2024.3452970","url":null,"abstract":"Presents corrections to the paper, Gastric Section Correlation Network for Gastric Precancerous Lesion Diagnosis.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10750350","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-08DOI: 10.1109/OJEMB.2024.3387893
{"title":"IEEE Open Journal of Engineering in Medicine and Biology Author Instructions","authors":"","doi":"10.1109/OJEMB.2024.3387893","DOIUrl":"https://doi.org/10.1109/OJEMB.2024.3387893","url":null,"abstract":"","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10747777","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-08DOI: 10.1109/OJEMB.2024.3387895
{"title":"IEEE Open Journal of Engineering in Medicine and Biology Editorial Board Information","authors":"","doi":"10.1109/OJEMB.2024.3387895","DOIUrl":"https://doi.org/10.1109/OJEMB.2024.3387895","url":null,"abstract":"","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10747779","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-07DOI: 10.1109/OJEMB.2024.3486457
Riccardo Barbieri;Maximiliano Mollura
{"title":"Guest Editorial: Introduction to the Special Series on Advances in Cardiovascular and Respiratory Systems Engineering","authors":"Riccardo Barbieri;Maximiliano Mollura","doi":"10.1109/OJEMB.2024.3486457","DOIUrl":"https://doi.org/10.1109/OJEMB.2024.3486457","url":null,"abstract":"","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10746532","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Goal: Organ perfusion is a vast subject with several techniques, the most common being Hypothermic Machine Perfusion (HMP) and Normothermic Machine Perfusion (NMP). In this paper, a new machine developed at the Biolab of the University of Florence has been tested to validate its capability to perform a well-controlled Oxygenated Rewarming (COR) phase and maintain stability during the NMP phase. Methods: The tests (n = 5) were conducted on fresh porcine livers and evaluated according to liver perfusion standards. The developed machine is based on a previous version, with an integrated control and sensor system and a complete mechanical and electronic redesign. Results: The results demonstrate the excellent usability of the machine and its ability to effectively maintain the organs in good condition. The new system performed well, and the measures made on the livers were satisfactory for good preservation of the organ. Conclusion: This study showed the effectiveness of the developed machine. Future development of the system will include a more sophisticated control system to ensure the correct parameters for perfusion.
{"title":"Experimental Evaluation of a New Perfusion Machine Using Normothermic Cycles on Explanted Livers","authors":"Eleonora Barcali;Lorenzo Maggi;Rebecca Panconesi;Fabio Staderini;Leonardo Bocchi;Cosimo Nardi;Nadia Navari;Adriano Peris;Matteo Risaliti;Mauricio F. Carvalho;Fabio Marra;Philipp Dutkowski;Gian Luca Grazi;Andrea Schlegel;Filippo Bigi;Mattia Dimitri;Andrea Corvi","doi":"10.1109/OJEMB.2024.3478791","DOIUrl":"https://doi.org/10.1109/OJEMB.2024.3478791","url":null,"abstract":"<italic>Goal:</i>\u0000 Organ perfusion is a vast subject with several techniques, the most common being Hypothermic Machine Perfusion (HMP) and Normothermic Machine Perfusion (NMP). In this paper, a new machine developed at the Biolab of the University of Florence has been tested to validate its capability to perform a well-controlled Oxygenated Rewarming (COR) phase and maintain stability during the NMP phase. \u0000<italic>Methods:</i>\u0000 The tests (n = 5) were conducted on fresh porcine livers and evaluated according to liver perfusion standards. The developed machine is based on a previous version, with an integrated control and sensor system and a complete mechanical and electronic redesign. \u0000<italic>Results:</i>\u0000 The results demonstrate the excellent usability of the machine and its ability to effectively maintain the organs in good condition. The new system performed well, and the measures made on the livers were satisfactory for good preservation of the organ. \u0000<italic>Conclusion:</i>\u0000 This study showed the effectiveness of the developed machine. Future development of the system will include a more sophisticated control system to ensure the correct parameters for perfusion.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10713905","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-09DOI: 10.1109/OJEMB.2024.3477267
Patrick Kiele;Gregor Laengle;Martin Schmoll;Cristian Pasluosta;Ronny Pfeifer;Martin Schuettler;Oskar Aszmann;Thomas Stieglitz
Goal: Transcutaneous coupling scheme for wireless powering and signal in active implants are known for more than a decade. This study aimed to investigate the in vivo behavior of this approach to drive multiple channels of an implanted peripheral nerve interfaces. Methods: The stimulation signals were transmitted through the skin over two contacts to an intracorporeal counterpart which was connected to a cuff electrode with two channels. EMG after stimulation was measured to establish recruitment curves. Results: Limitations of transcutaneous coupling were found in the feasible complexity of the system. High electrical crosstalk in a multi-channel system reduces this approach to low channel applications, such as pain treatment. No significant influence of the pulse width or extracorporeal stimulation amplitude on the electrical crosstalk was observed. Conclusions: The study's findings provide insight into the behavior of the transcutaneous coupling scheme in vivo and highlight the limitations and areas of application. Our results indicate that transcutaneous coupling schemes are a promising alternative approach for wireless powering of implants, as it does not require complex implanted electronics, expensive sophisticated electronics, and hermetic enclosures. Physical constraints, however, limit the use in highly selective nerve stimulation scenarios.
{"title":"Neural Implants Without Active Implanted Electronics: Possibilities and Limitations of Transcutaneous Coupling in Miniaturized Active Implants","authors":"Patrick Kiele;Gregor Laengle;Martin Schmoll;Cristian Pasluosta;Ronny Pfeifer;Martin Schuettler;Oskar Aszmann;Thomas Stieglitz","doi":"10.1109/OJEMB.2024.3477267","DOIUrl":"https://doi.org/10.1109/OJEMB.2024.3477267","url":null,"abstract":"<italic>Goal:</i>\u0000 Transcutaneous coupling scheme for wireless powering and signal in active implants are known for more than a decade. This study aimed to investigate the in vivo behavior of this approach to drive multiple channels of an implanted peripheral nerve interfaces. \u0000<italic>Methods:</i>\u0000 The stimulation signals were transmitted through the skin over two contacts to an intracorporeal counterpart which was connected to a cuff electrode with two channels. EMG after stimulation was measured to establish recruitment curves. \u0000<italic>Results:</i>\u0000 Limitations of transcutaneous coupling were found in the feasible complexity of the system. High electrical crosstalk in a multi-channel system reduces this approach to low channel applications, such as pain treatment. No significant influence of the pulse width or extracorporeal stimulation amplitude on the electrical crosstalk was observed. \u0000<italic>Conclusions:</i>\u0000 The study's findings provide insight into the behavior of the transcutaneous coupling scheme in vivo and highlight the limitations and areas of application. Our results indicate that transcutaneous coupling schemes are a promising alternative approach for wireless powering of implants, as it does not require complex implanted electronics, expensive sophisticated electronics, and hermetic enclosures. Physical constraints, however, limit the use in highly selective nerve stimulation scenarios.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10710177","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-08DOI: 10.1109/OJEMB.2024.3475911
Collin D. Bowersock;Zachary F. Lerner
Objective: This pilot study investigated the feasibility and efficacy of using autonomous ankle exoskeletons in community settings among individuals with cerebral palsy (CP). Five participants completed two structured community walking protocols: a week-long ankle exoskeleton acclimation and training intervention, and a dose-matched Sham intervention of unassisted walking. Results: Results demonstrated significant improvements in acclimatized walking performance with the ankle exoskeleton, including increased speed and stride length. Participants also reported increased enjoyment and perceived benefits of using the exoskeleton. While ankle exoskeleton training did not lead to significant improvements in unassisted walking, this study demonstrates the feasibility of using ankle exoskeletons in the real world by people with CP. Conclusions: This study highlights the potential of wearable exoskeletons to augment community walking performance in CP, laying a foundation for further exploration in real-world environments.
{"title":"Feasibility of Using Autonomous Ankle Exoskeletons to Augment Community Walking in Cerebral Palsy","authors":"Collin D. Bowersock;Zachary F. Lerner","doi":"10.1109/OJEMB.2024.3475911","DOIUrl":"https://doi.org/10.1109/OJEMB.2024.3475911","url":null,"abstract":"<italic>Objective:</i>\u0000 This pilot study investigated the feasibility and efficacy of using autonomous ankle exoskeletons in community settings among individuals with cerebral palsy (CP). Five participants completed two structured community walking protocols: a week-long ankle exoskeleton acclimation and training intervention, and a dose-matched Sham intervention of unassisted walking. \u0000<italic>Results:</i>\u0000 Results demonstrated significant improvements in acclimatized walking performance with the ankle exoskeleton, including increased speed and stride length. Participants also reported increased enjoyment and perceived benefits of using the exoskeleton. While ankle exoskeleton training did not lead to significant improvements in unassisted walking, this study demonstrates the feasibility of using ankle exoskeletons in the real world by people with CP. \u0000<italic>Conclusions:</i>\u0000 This study highlights the potential of wearable exoskeletons to augment community walking performance in CP, laying a foundation for further exploration in real-world environments.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10709375","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Goal: Respiration-correlated cone-beam computed tomography (4D-CBCT) is an X-ray-based imaging modality that uses reconstruction algorithms to produce time-varying volumetric images of moving anatomy over a cycle of respiratory motion. The quality of the produced images is affected by the number of CBCT projections available for reconstruction. Interpolation techniques have been used to generate intermediary projections to be used, along with the original projections, for reconstruction. Transfer learning is a powerful approach that harnesses the ability to reuse pre-trained models in solving new problems. Methods: Several state-of-the-art pre-trained deep learning models, used for video frame interpolation, are utilized in this work to generate intermediary projections. Moreover, a novel regression predictive modeling approach is also proposed to achieve the same objective. Digital phantom and clinical datasets are used to evaluate the performance of the models. Results: The results show that the Real-Time Intermediate Flow Estimation (RIFE) algorithm outperforms the others in terms of the Structural Similarity Index Method (SSIM): 0.986 $pm$ 0.010, Peak Signal to Noise Ratio (PSNR): 44.13 $pm$ 2.76, and Mean Square Error (MSE): 18.86 $pm$ 206.90 across all datasets. Moreover, the interpolated projections were used along with the original ones to reconstruct a 4D-CBCT image that was compared to that reconstructed from the original projections only. Conclusions: The reconstructed image using the proposed approach was found to minimize the streaking artifacts, thereby enhancing the image quality. This work demonstrates the advantage of using general-purpose transfer learning algorithms in 4D-CBCT image enhancement.
{"title":"Machine Learning-Based X-Ray Projection Interpolation for Improved 4D-CBCT Reconstruction","authors":"Jayroop Ramesh;Donthi Sankalpa;Rohan Mitra;Salam Dhou","doi":"10.1109/OJEMB.2024.3459622","DOIUrl":"10.1109/OJEMB.2024.3459622","url":null,"abstract":"<italic>Goal:</i>\u0000 Respiration-correlated cone-beam computed tomography (4D-CBCT) is an X-ray-based imaging modality that uses reconstruction algorithms to produce time-varying volumetric images of moving anatomy over a cycle of respiratory motion. The quality of the produced images is affected by the number of CBCT projections available for reconstruction. Interpolation techniques have been used to generate intermediary projections to be used, along with the original projections, for reconstruction. Transfer learning is a powerful approach that harnesses the ability to reuse pre-trained models in solving new problems. \u0000<italic>Methods:</i>\u0000 Several state-of-the-art pre-trained deep learning models, used for video frame interpolation, are utilized in this work to generate intermediary projections. Moreover, a novel regression predictive modeling approach is also proposed to achieve the same objective. Digital phantom and clinical datasets are used to evaluate the performance of the models. \u0000<italic>Results:</i>\u0000 The results show that the Real-Time Intermediate Flow Estimation (RIFE) algorithm outperforms the others in terms of the Structural Similarity Index Method (SSIM): 0.986 \u0000<inline-formula><tex-math>$pm$</tex-math></inline-formula>\u0000 0.010, Peak Signal to Noise Ratio (PSNR): 44.13 \u0000<inline-formula><tex-math>$pm$</tex-math></inline-formula>\u0000 2.76, and Mean Square Error (MSE): 18.86 \u0000<inline-formula><tex-math>$pm$</tex-math></inline-formula>\u0000 206.90 across all datasets. Moreover, the interpolated projections were used along with the original ones to reconstruct a 4D-CBCT image that was compared to that reconstructed from the original projections only. \u0000<italic>Conclusions:</i>\u0000 The reconstructed image using the proposed approach was found to minimize the streaking artifacts, thereby enhancing the image quality. This work demonstrates the advantage of using general-purpose transfer learning algorithms in 4D-CBCT image enhancement.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10678916","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142225756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Future space exploration missions will expose astronauts to various stressors, making the early detection of mental stress crucial for prolonged missions. Our study proposes using functional near infrared spectroscopy (fNIRS) combined with multiple machine learning models to assess the level of mental stress. Objective: The objective is to identify and quantify stress levels during 240 days confinement scenario. In this study, we utilize a diverse set of stress indicators including salivary alpha amylase (sAA) levels, reaction time (RT) to stimuli, accuracy of target detection, and power spectral density (PSD), in conjunction with functional connectivity networks (FCN). We estimate the PSD using Fast Fourier Transform (FFT) and the FCN using partial directed coherence. Results: Our findings reveal several intriguing insights. The sAA levels increased from the first 30 days in confinement to the culmination of the lengthy 240-day mission, suggesting a cumulative impact of stress. Conversely, RT and the accuracy of target detection exhibit significant fluctuations over the course of the mission. The power spectral density shows a significant increase with time-in-mission across all participants in most of the frontal area. The FCN shows a significant decrease in most of the right frontal areas. Five different machine learning classifiers are employed to differentiate between two levels of stress resulting in impressive classification accuracy rates: 96.44% with-nearest neighbor (KNN), 95.52% with linear discriminant analysis (LDA), 88.71% with Naïve Bayes (NB), 87.41 with decision trees (DT) and 96.48% with Support Vector Machine (SVM). In conclusion, this study demonstrates the effectiveness of combining functional near infrared spectroscopy (fNIRS) with multiple machine learning models to accurately assess and quantify mental stress levels during prolonged space missions, providing a promising approach for early stress detection in astronauts.
{"title":"Assessment of Brain Function After 240 Days Confinement Using Functional Near Infrared Spectroscopy","authors":"Fares Al-Shargie;Usman Tariq;Saleh Al-Ameri;Abdulla Al-Hammadi;Schastlivtseva Daria Vladimirovna;Hasan Al-Nashash","doi":"10.1109/OJEMB.2024.3457240","DOIUrl":"10.1109/OJEMB.2024.3457240","url":null,"abstract":"Future space exploration missions will expose astronauts to various stressors, making the early detection of mental stress crucial for prolonged missions. Our study proposes using functional near infrared spectroscopy (fNIRS) combined with multiple machine learning models to assess the level of mental stress. \u0000<italic>Objective:</i>\u0000 The objective is to identify and quantify stress levels during 240 days confinement scenario. In this study, we utilize a diverse set of stress indicators including salivary alpha amylase (sAA) levels, reaction time (RT) to stimuli, accuracy of target detection, and power spectral density (PSD), in conjunction with functional connectivity networks (FCN). We estimate the PSD using Fast Fourier Transform (FFT) and the FCN using partial directed coherence. \u0000<italic>Results:</i>\u0000 Our findings reveal several intriguing insights. The sAA levels increased from the first 30 days in confinement to the culmination of the lengthy 240-day mission, suggesting a cumulative impact of stress. Conversely, RT and the accuracy of target detection exhibit significant fluctuations over the course of the mission. The power spectral density shows a significant increase with time-in-mission across all participants in most of the frontal area. The FCN shows a significant decrease in most of the right frontal areas. Five different machine learning classifiers are employed to differentiate between two levels of stress resulting in impressive classification accuracy rates: 96.44% with-nearest neighbor (KNN), 95.52% with linear discriminant analysis (LDA), 88.71% with Naïve Bayes (NB), 87.41 with decision trees (DT) and 96.48% with Support Vector Machine (SVM). In conclusion, this study demonstrates the effectiveness of combining functional near infrared spectroscopy (fNIRS) with multiple machine learning models to accurately assess and quantify mental stress levels during prolonged space missions, providing a promising approach for early stress detection in astronauts.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10670317","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142200295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-09DOI: 10.1109/OJEMB.2024.3455801
Mohammed Salman;Pradeep Kumar Das;Sanjay Kumar Mohanty
Infectious diseases are a major global public health concern. Precise modeling and prediction methods are essential to develop effective strategies for disease control. However, data imbalance and the presence of noise and intensity inhomogeneity make disease detection more challenging. Goal: In this article, a novel infectious disease pattern prediction system is proposed by integrating deterministic and stochastic model benefits with the benefits of the deep learning model. Results: The combined benefits yield improvement in the performance of solution prediction. Moreover, the objective is also to investigate the influence of time delay on infection rates and rates associated with vaccination. Conclusions: In this proposed framework, at first, the global stability at disease free equilibrium is effectively analysed using Routh-Haurwitz criteria and Lyapunov method, and the endemic equilibrium is analysed using non-linear Volterra integral equations in the infectious disease model. Unlike the existing model, emphasis is given to suggesting a model that is capable of investigating stability while considering the effect of vaccination and migration rate. Next, the influence of vaccination on the rate of infection is effectively predicted using an efficient deep learning model by employing the long-term dependencies in sequential data. Thus making the prediction more accurate.
{"title":"An Integrated Framework for Infectious Disease Control Using Mathematical Modeling and Deep Learning","authors":"Mohammed Salman;Pradeep Kumar Das;Sanjay Kumar Mohanty","doi":"10.1109/OJEMB.2024.3455801","DOIUrl":"10.1109/OJEMB.2024.3455801","url":null,"abstract":"Infectious diseases are a major global public health concern. Precise modeling and prediction methods are essential to develop effective strategies for disease control. However, data imbalance and the presence of noise and intensity inhomogeneity make disease detection more challenging. \u0000<italic>Goal:</i>\u0000 In this article, a novel infectious disease pattern prediction system is proposed by integrating deterministic and stochastic model benefits with the benefits of the deep learning model. \u0000<italic>Results:</i>\u0000 The combined benefits yield improvement in the performance of solution prediction. Moreover, the objective is also to investigate the influence of time delay on infection rates and rates associated with vaccination. \u0000<italic>Conclusions:</i>\u0000 In this proposed framework, at first, the global stability at disease free equilibrium is effectively analysed using Routh-Haurwitz criteria and Lyapunov method, and the endemic equilibrium is analysed using non-linear Volterra integral equations in the infectious disease model. Unlike the existing model, emphasis is given to suggesting a model that is capable of investigating stability while considering the effect of vaccination and migration rate. Next, the influence of vaccination on the rate of infection is effectively predicted using an efficient deep learning model by employing the long-term dependencies in sequential data. Thus making the prediction more accurate.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10669273","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142200297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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