Pub Date : 2023-02-07DOI: 10.1109/RBME.2023.3242261
Huiqi Y. Lu;Xiaorong Ding;Jane E. Hirst;Yang Yang;Jenny Yang;Lucy Mackillop;David A. Clifton
Innovations in digital health and machine learning are changing the path of clinical health and care. People from different geographical locations and cultural backgrounds can benefit from the mobility of wearable devices and smartphones to monitor their health ubiquitously. This paper focuses on reviewing the digital health and machine learning technologies used in gestational diabetes – a subtype of diabetes that occurs during pregnancy. This paper reviews sensor technologies used in blood glucose monitoring devices, digital health innovations and machine learning models for gestational diabetes monitoring and management, in clinical and commercial settings, and discusses future directions. Despite one in six mothers having gestational diabetes, digital health applications were underdeveloped, especially the techniques that can be deployed in clinical practice. There is an urgent need to (1) develop clinically interpretable machine learning methods for patients with gestational diabetes, assisting health professionals with treatment, monitoring, and risk stratification before, during and after their pregnancies; (2) adapt and develop clinically-proven devices for patient self-management of health and well-being at home settings (“virtual ward” and virtual consultation), thereby improving clinical outcomes by facilitating timely intervention; and (3) ensure innovations are affordable and sustainable for all women with different socioeconomic backgrounds and clinical resources.
{"title":"Digital Health and Machine Learning Technologies for Blood Glucose Monitoring and Management of Gestational Diabetes","authors":"Huiqi Y. Lu;Xiaorong Ding;Jane E. Hirst;Yang Yang;Jenny Yang;Lucy Mackillop;David A. Clifton","doi":"10.1109/RBME.2023.3242261","DOIUrl":"10.1109/RBME.2023.3242261","url":null,"abstract":"Innovations in digital health and machine learning are changing the path of clinical health and care. People from different geographical locations and cultural backgrounds can benefit from the mobility of wearable devices and smartphones to monitor their health ubiquitously. This paper focuses on reviewing the digital health and machine learning technologies used in gestational diabetes – a subtype of diabetes that occurs during pregnancy. This paper reviews sensor technologies used in blood glucose monitoring devices, digital health innovations and machine learning models for gestational diabetes monitoring and management, in clinical and commercial settings, and discusses future directions. Despite one in six mothers having gestational diabetes, digital health applications were underdeveloped, especially the techniques that can be deployed in clinical practice. There is an urgent need to (1) develop clinically interpretable machine learning methods for patients with gestational diabetes, assisting health professionals with treatment, monitoring, and risk stratification before, during and after their pregnancies; (2) adapt and develop clinically-proven devices for patient self-management of health and well-being at home settings (“virtual ward” and virtual consultation), thereby improving clinical outcomes by facilitating timely intervention; and (3) ensure innovations are affordable and sustainable for all women with different socioeconomic backgrounds and clinical resources.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"17 ","pages":"98-117"},"PeriodicalIF":17.6,"publicationDate":"2023-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10492955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-05DOI: 10.1109/RBME.2022.3228083
Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
列出本期出版物的编辑委员会、董事会、现任工作人员、委员会成员和/或协会编辑。
{"title":"IEEE Engineering in Medicine and Biology Society Information","authors":"","doi":"10.1109/RBME.2022.3228083","DOIUrl":"https://doi.org/10.1109/RBME.2022.3228083","url":null,"abstract":"Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"16 ","pages":"C2-C2"},"PeriodicalIF":17.6,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/4664312/10007429/10007531.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67744159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-05DOI: 10.1109/RBME.2022.3228079
These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.
这些说明为编写本出版物的论文提供了指导。为在本期刊上发表文章的作者提供信息。
{"title":"IEEE Reviews in Biomedical Engineering (R-BME) Information","authors":"","doi":"10.1109/RBME.2022.3228079","DOIUrl":"https://doi.org/10.1109/RBME.2022.3228079","url":null,"abstract":"These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"16 ","pages":"C3-C3"},"PeriodicalIF":17.6,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/4664312/10007429/10007529.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67744158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-07DOI: 10.1109/RBME.2022.3227337
Chang-Hoon Choi;Jörg Felder;Christoph Lerche;N. Jon Shah
Simultaneously operating MR-PET systems have the potential to provide synergetic multi-parametric information, and, as such, interest surrounding their use and development is increasing. However, despite the potential advantages offered by fully combined MR-PET systems, implementing this hybrid integration is technically laborious, and any factors degrading the quality of either modality must be circumvented to ensure optimal performance. In order to attain the best possible quality from both systems, most full MR-PET integrations tend to place the shielded PET system inside the MRI system, close to the target volume of the subject. The radiofrequency (RF) coil used in MRI systems is a key factor in determining the quality of the MR images, and, in simultaneous acquisition, it is generally positioned inside the PET system and PET imaging region, potentially resulting in attenuation and artefacts in the PET images. Therefore, when designing hybrid MR-PET systems, it is imperative that consideration be given to the RF coils inside the PET system. In this review, we present current state-of-the-art RF coil designs used for hybrid MR-PET experiments and discuss various design strategies for constructing PET transparent RF coils.
同时运行的 MR-PET 系统具有提供多参数协同信息的潜力，因此，人们对其使用和开发的兴趣与日俱增。然而，尽管完全联合的 MR-PET 系统具有潜在的优势，但实施这种混合集成在技术上非常费力，而且必须避免任何降低两种模式质量的因素，以确保最佳性能。为了使两种系统都能达到最佳质量，大多数全面的 MR-PET 集成系统都倾向于将屏蔽 PET 系统置于 MRI 系统内部，靠近受检者的目标容积。MRI 系统中使用的射频（RF）线圈是决定 MR 图像质量的关键因素，而在同步采集中，它通常位于 PET 系统和 PET 成像区域内，可能会导致 PET 图像的衰减和伪影。因此，在设计 MR-PET 混合系统时，必须考虑 PET 系统内的射频线圈。在本综述中，我们将介绍目前用于混合 MR-PET 实验的最先进的射频线圈设计，并讨论构建 PET 透明射频线圈的各种设计策略。
{"title":"MRI Coil Development Strategies for Hybrid MR-PET Systems: A Review","authors":"Chang-Hoon Choi;Jörg Felder;Christoph Lerche;N. Jon Shah","doi":"10.1109/RBME.2022.3227337","DOIUrl":"10.1109/RBME.2022.3227337","url":null,"abstract":"Simultaneously operating MR-PET systems have the potential to provide synergetic multi-parametric information, and, as such, interest surrounding their use and development is increasing. However, despite the potential advantages offered by fully combined MR-PET systems, implementing this hybrid integration is technically laborious, and any factors degrading the quality of either modality must be circumvented to ensure optimal performance. In order to attain the best possible quality from both systems, most full MR-PET integrations tend to place the shielded PET system inside the MRI system, close to the target volume of the subject. The radiofrequency (RF) coil used in MRI systems is a key factor in determining the quality of the MR images, and, in simultaneous acquisition, it is generally positioned inside the PET system and PET imaging region, potentially resulting in attenuation and artefacts in the PET images. Therefore, when designing hybrid MR-PET systems, it is imperative that consideration be given to the RF coils inside the PET system. In this review, we present current state-of-the-art RF coil designs used for hybrid MR-PET experiments and discuss various design strategies for constructing PET transparent RF coils.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"17 ","pages":"342-350"},"PeriodicalIF":17.6,"publicationDate":"2022-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9247689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-10DOI: 10.1109/RBME.2022.3221366
Bin He
Presents the introductory editorial for this issue of the publication.
介绍本期出版物的介绍性社论。
{"title":"Editorial A Message From the New Editor-in-Chief","authors":"Bin He","doi":"10.1109/RBME.2022.3221366","DOIUrl":"10.1109/RBME.2022.3221366","url":null,"abstract":"Presents the introductory editorial for this issue of the publication.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"16 ","pages":"4-4"},"PeriodicalIF":17.6,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/4664312/10007429/09944964.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9244831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-08DOI: 10.1109/RBME.2022.3220636
Leif Sörnmo;Raquel Bailón;Pablo Laguna
The tools for spectrally analyzing heart rate variability (HRV) has in recent years grown considerably, with emphasis on the handling of time-varying conditions and confounding factors. Time–frequency analysis holds since long an important position in HRV analysis, however, this technique cannot alone handle a mean heart rate or a respiratory frequency which vary over time. Overlapping frequency bands represents another critical condition which needs to be dealt with to produce accurate spectral measurements. The present survey offers a comprehensive account of techniques designed to handle such conditions and factors by providing a brief description of the main principles of the different methods. Several methods derive from a mathematical/statistical model, suggesting that the model can be used to simulate data used for performance evaluation. The inclusion of a respiratory signal, whether measured or derived, is another feature of many recent methods, e.g., used to guide the decomposition of the HRV signal so that signals related as well as unrelated to respiration can be analyzed. It is concluded that the development of new approaches to handling time-varying scenarios are warranted, as is benchmarking of performance evaluated in technical as well as in physiological/clinical terms.
{"title":"Spectral Analysis of Heart Rate Variability in Time-Varying Conditions and in the Presence of Confounding Factors","authors":"Leif Sörnmo;Raquel Bailón;Pablo Laguna","doi":"10.1109/RBME.2022.3220636","DOIUrl":"10.1109/RBME.2022.3220636","url":null,"abstract":"The tools for spectrally analyzing heart rate variability (HRV) has in recent years grown considerably, with emphasis on the handling of time-varying conditions and confounding factors. Time–frequency analysis holds since long an important position in HRV analysis, however, this technique cannot alone handle a mean heart rate or a respiratory frequency which vary over time. Overlapping frequency bands represents another critical condition which needs to be dealt with to produce accurate spectral measurements. The present survey offers a comprehensive account of techniques designed to handle such conditions and factors by providing a brief description of the main principles of the different methods. Several methods derive from a mathematical/statistical model, suggesting that the model can be used to simulate data used for performance evaluation. The inclusion of a respiratory signal, whether measured or derived, is another feature of many recent methods, e.g., used to guide the decomposition of the HRV signal so that signals related as well as unrelated to respiration can be analyzed. It is concluded that the development of new approaches to handling time-varying scenarios are warranted, as is benchmarking of performance evaluated in technical as well as in physiological/clinical terms.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"17 ","pages":"322-341"},"PeriodicalIF":17.6,"publicationDate":"2022-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40451706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-08DOI: 10.1109/RBME.2022.3220505
Alireza Golgouneh;Lucy E. Dunne
Body compression through a garment or inflatable pneumatic mechanism has various applications in aesthetic, athletic, robotics, haptics, astronautics, and especially medical fields for treatment of various disorders such as varicose veins, lymphedema, deep vein thrombosis, and orthostatic intolerance. Traditionally, compression has been done through under-sized (e.g. elastic) or size-adjustable (e.g. inflatable) compression garments. Such systems are designed to apply substantially uniform pressure on the body. However, due to reasons such as anatomical variations and body posture change, different levels of compression may be applied to the body. Further, a high level of discomfort and non-compliance is reported among patients due to donning difficulties. Therefore, there have been some efforts to make compression garments smart by employing advanced functional soft materials and actuators (such as Shape Memory Alloy (SMA), Shape Memory Polymer (SMP), Electroactive polymer (EAP), etc.) as well as soft force-pressure sensors so that the compression level could be controlled and regulated for each person or specific tasks. However, despite these advances, there are still challenges to accurately controlling the on-body compression level that are mainly due to the inherent characteristics of the soft actuators or sensors and the sophisticated human body conditions. In this paper, we will first investigate the soft actuators and sensors that have the potential to be used for on-body compression applications. Then, integrated soft sensing-actuation systems for interfacial compression purposes are studied. Finally, the challenges that might be associated with this work are introduced.
{"title":"A Review in On-Body Compression Using Soft Actuators and Sensors: Applications, Mechanisms, and Challenges","authors":"Alireza Golgouneh;Lucy E. Dunne","doi":"10.1109/RBME.2022.3220505","DOIUrl":"10.1109/RBME.2022.3220505","url":null,"abstract":"Body compression through a garment or inflatable pneumatic mechanism has various applications in aesthetic, athletic, robotics, haptics, astronautics, and especially medical fields for treatment of various disorders such as varicose veins, lymphedema, deep vein thrombosis, and orthostatic intolerance. Traditionally, compression has been done through under-sized (e.g. elastic) or size-adjustable (e.g. inflatable) compression garments. Such systems are designed to apply substantially uniform pressure on the body. However, due to reasons such as anatomical variations and body posture change, different levels of compression may be applied to the body. Further, a high level of discomfort and non-compliance is reported among patients due to donning difficulties. Therefore, there have been some efforts to make compression garments smart by employing advanced functional soft materials and actuators (such as Shape Memory Alloy (SMA), Shape Memory Polymer (SMP), Electroactive polymer (EAP), etc.) as well as soft force-pressure sensors so that the compression level could be controlled and regulated for each person or specific tasks. However, despite these advances, there are still challenges to accurately controlling the on-body compression level that are mainly due to the inherent characteristics of the soft actuators or sensors and the sophisticated human body conditions. In this paper, we will first investigate the soft actuators and sensors that have the potential to be used for on-body compression applications. Then, integrated soft sensing-actuation systems for interfacial compression purposes are studied. Finally, the challenges that might be associated with this work are introduced.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"17 ","pages":"166-179"},"PeriodicalIF":17.6,"publicationDate":"2022-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40451707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-04DOI: 10.1109/RBME.2022.3219433
Maggie Ezzat Gaber Gendy;Mehmet Rasit Yuce
Confronted with the COVID-19 health crisis, the year 2020 represented a turning point for the entire world. It paved the way for health-care systems to reaffirm their foundations by using different technologies such as sensors, wearables, mobile applications, drones, robots, Artificial Intelligence (AI), Machine Learning (ML) and the Internet of Things (IoT). A lot of domains have been renovated such as diagnosis, treatment, and monitoring, as well as previously unprecedented domains such as contact tracing. Contact tracing, in conjunction with the emergence, spread, and public compliance for vaccines, was a critical step for controlling and limiting the spread of the pandemic. Traditional contact tracing is usually dependent on individuals ability to recall their interactions, which is challenging and yet not effective. Consequently, further development and usage of automated, privacy-preserving, digital contact-tracing was required. As the pandemic is coming to an end, it is vital to collect and learn the effective used technologies that aided in fighting the virus in order to be prepared for any future pandemics and to be aware of any literature gaps that must be filled. This paper surveys state-of-the-art architectures, platforms, and applications combating COVID-19 at each phase of the five basic contact tracing phases, including case identification, contacts identification and rapid exposure notification, surveillance, regular follow up and prevention. In addition, there is a phase of preparation and post-pandemic services for current and needed future technology that will aid in the fight against any incoming infectious diseases.
{"title":"Emerging Technologies Used in Health Management and Efficiency Improvement During Different Contact Tracing Phases Against COVID-19 Pandemic","authors":"Maggie Ezzat Gaber Gendy;Mehmet Rasit Yuce","doi":"10.1109/RBME.2022.3219433","DOIUrl":"10.1109/RBME.2022.3219433","url":null,"abstract":"Confronted with the COVID-19 health crisis, the year 2020 represented a turning point for the entire world. It paved the way for health-care systems to reaffirm their foundations by using different technologies such as sensors, wearables, mobile applications, drones, robots, Artificial Intelligence (AI), Machine Learning (ML) and the Internet of Things (IoT). A lot of domains have been renovated such as diagnosis, treatment, and monitoring, as well as previously unprecedented domains such as contact tracing. Contact tracing, in conjunction with the emergence, spread, and public compliance for vaccines, was a critical step for controlling and limiting the spread of the pandemic. Traditional contact tracing is usually dependent on individuals ability to recall their interactions, which is challenging and yet not effective. Consequently, further development and usage of automated, privacy-preserving, digital contact-tracing was required. As the pandemic is coming to an end, it is vital to collect and learn the effective used technologies that aided in fighting the virus in order to be prepared for any future pandemics and to be aware of any literature gaps that must be filled. This paper surveys state-of-the-art architectures, platforms, and applications combating COVID-19 at each phase of the five basic contact tracing phases, including case identification, contacts identification and rapid exposure notification, surveillance, regular follow up and prevention. In addition, there is a phase of preparation and post-pandemic services for current and needed future technology that will aid in the fight against any incoming infectious diseases.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"16 ","pages":"38-52"},"PeriodicalIF":17.6,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9359277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-27DOI: 10.1109/RBME.2022.3217486
David Choy Buentello;Mariana García-Corral;Grissel Trujillo-de Santiago;Mario Moisés Alvarez
Neuron-on-chip (NoC) systems—microfluidic devices in which neurons are cultured—have become a promising alternative to replace or minimize the use of animal models and have greatly facilitated in vitro research. Here, we review and discuss current developments in neuron-on-chip platforms, with a particular emphasis on existing biological models, culturing techniques, biomaterials, and topologies. We also discuss how the architecture, flow, and gradients affect neuronal growth, differentiation, and development. Finally, we discuss some of the most recent applications of NoCs in fundamental research (i.e., studies on the effects of electrical, mechanical/topological, or chemical stimuli) and in disease modeling.
{"title":"Neuron(s)-on-a-Chip: A Review of the Design and Use of Microfluidic Systems for Neural Tissue Culture","authors":"David Choy Buentello;Mariana García-Corral;Grissel Trujillo-de Santiago;Mario Moisés Alvarez","doi":"10.1109/RBME.2022.3217486","DOIUrl":"10.1109/RBME.2022.3217486","url":null,"abstract":"Neuron-on-chip (NoC) systems—microfluidic devices in which neurons are cultured—have become a promising alternative to replace or minimize the use of animal models and have greatly facilitated in vitro research. Here, we review and discuss current developments in neuron-on-chip platforms, with a particular emphasis on existing biological models, culturing techniques, biomaterials, and topologies. We also discuss how the architecture, flow, and gradients affect neuronal growth, differentiation, and development. Finally, we discuss some of the most recent applications of NoCs in fundamental research (i.e., studies on the effects of electrical, mechanical/topological, or chemical stimuli) and in disease modeling.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"17 ","pages":"243-263"},"PeriodicalIF":17.6,"publicationDate":"2022-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9255690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-21DOI: 10.1109/RBME.2022.3216531
Monica Isgut;Logan Gloster;Katherine Choi;Janani Venugopalan;May D. Wang
At the beginning of the COVID-19 pandemic, there was significant hype about the potential impact of artificial intelligence (AI) tools in combatting COVID-19 on diagnosis, prognosis, or surveillance. However, AI tools have not yet been widely successful. One of the key reason is the COVID-19 pandemic has demanded faster real-time development of AI-driven clinical and health support tools, including rapid data collection, algorithm development, validation, and deployment. However, there was not enough time for proper data quality control. Learning from the hard lessons in COVID-19, we summarize the important health data quality challenges during COVID-19 pandemic such as lack of data standardization, missing data, tabulation errors, and noise and artifact. Then we conduct a systematic investigation of computational methods that address these issues, including emerging novel advanced AI data quality control methods that achieve better data quality outcomes and, in some cases, simplify or automate the data cleaning process. We hope this article can assist healthcare community to improve health data quality going forward with novel AI development.
{"title":"Systematic Review of Advanced AI Methods for Improving Healthcare Data Quality in Post COVID-19 Era","authors":"Monica Isgut;Logan Gloster;Katherine Choi;Janani Venugopalan;May D. Wang","doi":"10.1109/RBME.2022.3216531","DOIUrl":"10.1109/RBME.2022.3216531","url":null,"abstract":"At the beginning of the COVID-19 pandemic, there was significant hype about the potential impact of artificial intelligence (AI) tools in combatting COVID-19 on diagnosis, prognosis, or surveillance. However, AI tools have not yet been widely successful. One of the key reason is the COVID-19 pandemic has demanded faster real-time development of AI-driven clinical and health support tools, including rapid data collection, algorithm development, validation, and deployment. However, there was not enough time for proper data quality control. Learning from the hard lessons in COVID-19, we summarize the important health data quality challenges during COVID-19 pandemic such as lack of data standardization, missing data, tabulation errors, and noise and artifact. Then we conduct a systematic investigation of computational methods that address these issues, including emerging novel advanced AI data quality control methods that achieve better data quality outcomes and, in some cases, simplify or automate the data cleaning process. We hope this article can assist healthcare community to improve health data quality going forward with novel AI development.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"16 ","pages":"53-69"},"PeriodicalIF":17.6,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/4664312/10007429/09926151.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9728920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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