The lungs are the main fundamental part of the human respiratory system and are among the major organs of the human body.Lung disorders,including Coronavirus(Covid-19),are among the world’s deadliest and most life-th...The lungs are the main fundamental part of the human respiratory system and are among the major organs of the human body.Lung disorders,including Coronavirus(Covid-19),are among the world’s deadliest and most life-threatening diseases.Early and social distance-based detection and treatment can save lives as well as protect the rest of humanity.Even though X-rays or Computed Tomography(CT)scans are the imaging techniques to analyze lung-related disorders,medical practitioners still find it challenging to analyze and identify lung cancer from scanned images.unless COVID-19 reaches the lungs,it is unable to be diagnosed.through these modalities.So,the Internet of Medical Things(IoMT)and machine learning-based computer-assisted approaches have been developed and applied to automate these diagnostic procedures.This study also aims at investigating an automated approach for the detection of COVID-19 and lung disorders other than COVID-19 infection in a non-invasive manner at their early stages through the analysis of human breath.Human breath contains several volatile organic compounds,i.e.,water vapor(5.0%–6.3%),nitrogen(79%),oxygen(13.6%–16.0%),carbon dioxide(4.0%–5.3%),argon(1%),hydro-gen(1 ppm)(parts per million),carbon monoxide(1%),proteins(1%),isoprene(1%),acetone(1%),and ammonia(1%).Beyond these limits,the presence of a certain volatile organic compound(VOC)may indicate a disease.The proposed research not only aims to increase the accuracy of lung disorder detection from breath analysis but also to deploy the model in a real-time environment as a home appliance.Different sensors detect VOC;microcontrollers and machine learning models have been used to detect these lung disorders.Overall,the suggested methodology is accurate,efficient,and non-invasive.The proposed method obtained an accuracy of 93.59%,a sensitivity of 89.59%,a specificity of 94.87%,and an AUC-Value of 0.96.展开更多
Federated learning has recently attracted significant attention as a cutting-edge technology that enables Artificial Intelligence(AI)algorithms to utilize global learning across the data of numerous individuals while ...Federated learning has recently attracted significant attention as a cutting-edge technology that enables Artificial Intelligence(AI)algorithms to utilize global learning across the data of numerous individuals while safeguarding user data privacy.Recent advanced healthcare technologies have enabled the early diagnosis of various cognitive ailments like Parkinson’s.Adequate user data is frequently used to train machine learning models for healthcare systems to track the health status of patients.The healthcare industry faces two significant challenges:security and privacy issues and the personalization of cloud-trained AI models.This paper proposes a Deep Neural Network(DNN)based approach embedded in a federated learning framework to detect and diagnose brain disorders.We extracted the data from the database of Kay Elemetrics voice disordered and divided the data into two windows to create training models for two clients,each with different data.To lessen the over-fitting aspect,every client reviewed the outcomes in three rounds.The proposed model identifies brain disorders without jeopardizing privacy and security.The results reveal that the global model achieves an accuracy of 82.82%for detecting brain disorders while preserving privacy.展开更多
A technique has been developed using PCR to detect monoclonality of B-lymphoproliferative disorders. DNA was extracted from the blood, tissue and paraffin embedded sections by biochemical means or boiling. Forty cases...A technique has been developed using PCR to detect monoclonality of B-lymphoproliferative disorders. DNA was extracted from the blood, tissue and paraffin embedded sections by biochemical means or boiling. Forty cases of B-non Hodgkin's lymphoma (NHL), 15 cases of T-NHL, 8 cases of chronic lymphocytic leukemia, 17 cases of reactive lymphadenopathy and 12 cases of various non-lym-phocytic tumor were examined. Monoclonality of B-lymphocytes was detected in 86-92% of cases with B-lymphoproliferative diseases, but none in T-NHL, reactive disorders and non-lymphatic tumors. This technique provides a new molecular biologic method to diagnose malignant B-lymphoproliferative dicor-ders. It may be useful in Ig gene rearrangement study, differential diagnosis and retrospective investigation of lymphoproliferative disorders.展开更多
Background:Major depressive disorder(MDD)and autism spectrum disorder(ASD)are complex and heterogeneous neuropsychiatric disorders with overlapping symptoms,presenting remarkable challenges for accurate diagnosis.Leve...Background:Major depressive disorder(MDD)and autism spectrum disorder(ASD)are complex and heterogeneous neuropsychiatric disorders with overlapping symptoms,presenting remarkable challenges for accurate diagnosis.Leveraging functional neuroimaging data offers an opportunity to develop more robust,data-driven approach for psychiatric disorder detection.However,existing methods often struggle to capture the long-term dependencies and dynamic patterns inherent in such data,particularly across diverse imaging sites.Methods:We propose Multiscale Contextual Mamba(MSC-Mamba),a Mamba-based model designed for capturing long-term dependencies in multivariate time-series data while maintaining linear scalability,allowing us to account for long-range interactions and subtle dynamic patterns within the brain’s functional networks.One of the main advantages of MSC-Mamba is its ability to leverage the distinct characteristics of time-series data,allowing it to generate meaningful contextual information across various scales.This method effectively addresses both channel-mixing and channel-independence scenarios,facilitating the selection of relevant features for prediction by considering both global and local contexts at multiple scales.Results:Two large-scale multisite functional magnetic resonance imaging datasets,including REST-meta-MDD(n=1,642)and Autism Brain Imaging Data Exchange(ABIDE)(n=1,022),were used to validate the performance of our proposed approach.MSC-Mamba has achieved stateof-the-art performance,with an accuracy of 69.91%for MDD detection and 73.08%for ASD detection.The results demonstrate the model’s robust generalization across imaging sites and its sensitivity to intricate brain network dynamics.Conclusions:This paper demonstrates the potential of state-space models in advancing psychiatric neuroimaging research.The findings suggest that such models can significantly enhance detection accuracy for MDD and ASD,pointing toward more reliable,data-driven diagnostic tools in psychiatric disorder detection.展开更多
文摘The lungs are the main fundamental part of the human respiratory system and are among the major organs of the human body.Lung disorders,including Coronavirus(Covid-19),are among the world’s deadliest and most life-threatening diseases.Early and social distance-based detection and treatment can save lives as well as protect the rest of humanity.Even though X-rays or Computed Tomography(CT)scans are the imaging techniques to analyze lung-related disorders,medical practitioners still find it challenging to analyze and identify lung cancer from scanned images.unless COVID-19 reaches the lungs,it is unable to be diagnosed.through these modalities.So,the Internet of Medical Things(IoMT)and machine learning-based computer-assisted approaches have been developed and applied to automate these diagnostic procedures.This study also aims at investigating an automated approach for the detection of COVID-19 and lung disorders other than COVID-19 infection in a non-invasive manner at their early stages through the analysis of human breath.Human breath contains several volatile organic compounds,i.e.,water vapor(5.0%–6.3%),nitrogen(79%),oxygen(13.6%–16.0%),carbon dioxide(4.0%–5.3%),argon(1%),hydro-gen(1 ppm)(parts per million),carbon monoxide(1%),proteins(1%),isoprene(1%),acetone(1%),and ammonia(1%).Beyond these limits,the presence of a certain volatile organic compound(VOC)may indicate a disease.The proposed research not only aims to increase the accuracy of lung disorder detection from breath analysis but also to deploy the model in a real-time environment as a home appliance.Different sensors detect VOC;microcontrollers and machine learning models have been used to detect these lung disorders.Overall,the suggested methodology is accurate,efficient,and non-invasive.The proposed method obtained an accuracy of 93.59%,a sensitivity of 89.59%,a specificity of 94.87%,and an AUC-Value of 0.96.
基金supported by the Deanship of Scientific Research at Prince Sattam bin Aziz University under the Research Project (PSAU/2023/01/22425).
文摘Federated learning has recently attracted significant attention as a cutting-edge technology that enables Artificial Intelligence(AI)algorithms to utilize global learning across the data of numerous individuals while safeguarding user data privacy.Recent advanced healthcare technologies have enabled the early diagnosis of various cognitive ailments like Parkinson’s.Adequate user data is frequently used to train machine learning models for healthcare systems to track the health status of patients.The healthcare industry faces two significant challenges:security and privacy issues and the personalization of cloud-trained AI models.This paper proposes a Deep Neural Network(DNN)based approach embedded in a federated learning framework to detect and diagnose brain disorders.We extracted the data from the database of Kay Elemetrics voice disordered and divided the data into two windows to create training models for two clients,each with different data.To lessen the over-fitting aspect,every client reviewed the outcomes in three rounds.The proposed model identifies brain disorders without jeopardizing privacy and security.The results reveal that the global model achieves an accuracy of 82.82%for detecting brain disorders while preserving privacy.
文摘A technique has been developed using PCR to detect monoclonality of B-lymphoproliferative disorders. DNA was extracted from the blood, tissue and paraffin embedded sections by biochemical means or boiling. Forty cases of B-non Hodgkin's lymphoma (NHL), 15 cases of T-NHL, 8 cases of chronic lymphocytic leukemia, 17 cases of reactive lymphadenopathy and 12 cases of various non-lym-phocytic tumor were examined. Monoclonality of B-lymphocytes was detected in 86-92% of cases with B-lymphoproliferative diseases, but none in T-NHL, reactive disorders and non-lymphatic tumors. This technique provides a new molecular biologic method to diagnose malignant B-lymphoproliferative dicor-ders. It may be useful in Ig gene rearrangement study, differential diagnosis and retrospective investigation of lymphoproliferative disorders.
基金supported by grants from the National Natural Science Foundation of P.R.China(62276081 and 62106113)the Guangdong Basic and Applied Basic Research Foundation(2023A1515010792 and 2023B1515120065)the Shenzhen Science and Technology Program(GXWD20231129121139001 and JCYJ20240813110522029).
文摘Background:Major depressive disorder(MDD)and autism spectrum disorder(ASD)are complex and heterogeneous neuropsychiatric disorders with overlapping symptoms,presenting remarkable challenges for accurate diagnosis.Leveraging functional neuroimaging data offers an opportunity to develop more robust,data-driven approach for psychiatric disorder detection.However,existing methods often struggle to capture the long-term dependencies and dynamic patterns inherent in such data,particularly across diverse imaging sites.Methods:We propose Multiscale Contextual Mamba(MSC-Mamba),a Mamba-based model designed for capturing long-term dependencies in multivariate time-series data while maintaining linear scalability,allowing us to account for long-range interactions and subtle dynamic patterns within the brain’s functional networks.One of the main advantages of MSC-Mamba is its ability to leverage the distinct characteristics of time-series data,allowing it to generate meaningful contextual information across various scales.This method effectively addresses both channel-mixing and channel-independence scenarios,facilitating the selection of relevant features for prediction by considering both global and local contexts at multiple scales.Results:Two large-scale multisite functional magnetic resonance imaging datasets,including REST-meta-MDD(n=1,642)and Autism Brain Imaging Data Exchange(ABIDE)(n=1,022),were used to validate the performance of our proposed approach.MSC-Mamba has achieved stateof-the-art performance,with an accuracy of 69.91%for MDD detection and 73.08%for ASD detection.The results demonstrate the model’s robust generalization across imaging sites and its sensitivity to intricate brain network dynamics.Conclusions:This paper demonstrates the potential of state-space models in advancing psychiatric neuroimaging research.The findings suggest that such models can significantly enhance detection accuracy for MDD and ASD,pointing toward more reliable,data-driven diagnostic tools in psychiatric disorder detection.
