Pub Date : 2024-02-08DOI: 10.1109/OJEMB.2024.3364075
Dieter Haemmerich;Punit Prakash
Numerous medical imaging modalities are clinically available and widely used in disease diagnosis and monitoring therapy response. Imaging data are also increasingly being used for constructing patient-specific computational models to inform design and evaluation of devices, and for personalized planning of interventions. Several of these imaging modalities – e.g., ultrasound imaging, computed tomography (CT), and magnetic resonance imaging (MRI) – can also provide real-time image data. In concert with advances in therapy delivery devices and systems, this real-time capability has enabled image-guided therapies, where multiple image series are acquired during a procedure and are used for therapy guidance. This special issue presents six research studies to highlight recent advances in this research area.
{"title":"Guest Editorial Introduction to the Special Section on Image-Guided Therapies","authors":"Dieter Haemmerich;Punit Prakash","doi":"10.1109/OJEMB.2024.3364075","DOIUrl":"https://doi.org/10.1109/OJEMB.2024.3364075","url":null,"abstract":"Numerous medical imaging modalities are clinically available and widely used in disease diagnosis and monitoring therapy response. Imaging data are also increasingly being used for constructing patient-specific computational models to inform design and evaluation of devices, and for personalized planning of interventions. Several of these imaging modalities – e.g., ultrasound imaging, computed tomography (CT), and magnetic resonance imaging (MRI) – can also provide real-time image data. In concert with advances in therapy delivery devices and systems, this real-time capability has enabled image-guided therapies, where multiple image series are acquired during a procedure and are used for therapy guidance. This special issue presents six research studies to highlight recent advances in this research area.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10428939","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139942786","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 : 2024-02-08DOI: 10.1109/OJEMB.2024.3364065
Samuel Dolphin;Maren Downing;Mia Cirrincione;Adam Samuta;Kevin Leite;Kimberly Noble;Brian Walsh
Braille is often proposed by the uninformed as the optimal solution to providing an alternative to visual information to the visually impaired. The purpose of this article is to highlight the complexity of the braille user population and discuss the importance of understanding the use of braille as a solution for equal access of information. As part of the National Institute of Biomedical Imaging and Bioengineering (NIBIB) Rapid Acceleration of Diagnostics (RADx) Tech program and its goal to make home tests accessible to people with disabilities, a series of interviews with industry experts was conducted to better understand braille technologies and the braille user space. Published literature findings provided additional context and support to these interviews. It was found that expert consensus and data from published literature vary. The braille user population is complex and lacks consistent characterization. Visually printed media should not be solely relied on to communicate information. In conclusion, braille is one solution for improving access to information. Understanding the unique needs of braille users and how they engage with information in a world that is heavily reliant on visual content, is a critical step in developing and implementing non-visual alternatives that will collectively address information access.
{"title":"Information Accessibility in the Form of Braille","authors":"Samuel Dolphin;Maren Downing;Mia Cirrincione;Adam Samuta;Kevin Leite;Kimberly Noble;Brian Walsh","doi":"10.1109/OJEMB.2024.3364065","DOIUrl":"10.1109/OJEMB.2024.3364065","url":null,"abstract":"Braille is often proposed by the uninformed as the optimal solution to providing an alternative to visual information to the visually impaired. The purpose of this article is to highlight the complexity of the braille user population and discuss the importance of understanding the use of braille as a solution for equal access of information. As part of the National Institute of Biomedical Imaging and Bioengineering (NIBIB) Rapid Acceleration of Diagnostics (RADx) Tech program and its goal to make home tests accessible to people with disabilities, a series of interviews with industry experts was conducted to better understand braille technologies and the braille user space. Published literature findings provided additional context and support to these interviews. It was found that expert consensus and data from published literature vary. The braille user population is complex and lacks consistent characterization. Visually printed media should not be solely relied on to communicate information. In conclusion, braille is one solution for improving access to information. Understanding the unique needs of braille users and how they engage with information in a world that is heavily reliant on visual content, is a critical step in developing and implementing non-visual alternatives that will collectively address information access.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10428077","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139949492","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 : 2024-02-07DOI: 10.1109/OJEMB.2024.3363137
Yasuhiro Kato;Toshiaki Tsuji;Imre Cikajlo
Haptic interfaces and virtual reality (VR) technology have been increasingly introduced in rehabilitation, facilitating the provision of various feedback and task conditions. However, correspondence between the feedback/task conditions and movement strategy during reaching tasks remains a question. To investigate movement strategy, we assessed velocity parameters and peak latency of electromyography. Ten neuromuscularly intact volunteers participated in the measurement using haptic interface and VR. Concurrent visual feedback and various terminal feedback (e.g., visual, haptic, visual and haptic) were given. Additionally, the object size for the reaching task was changed. The results demonstrated terminal haptic feedback had a significant impact on kinematic parameters; showed $0.7,pm {,1.4}$ s ( $p,< .05$ ) shorter movement time and $0.01,pm {,0.08}$ m/s ( $p,< .05$ ) higher mean velocity compared to no terminal feedback. Also, smaller peak latency was observed in different muscle regions based on the object size.
触觉界面和虚拟现实（VR）技术越来越多地被引入康复领域，为提供各种反馈和任务条件提供了便利。然而，在伸手任务中，反馈/任务条件与运动策略之间的对应关系仍然是一个问题。为了研究运动策略，我们评估了肌电图的速度参数和峰值潜伏期。十名神经肌肉健全的志愿者参与了使用触觉界面和 VR 进行的测量。我们同时提供了视觉反馈和各种终端反馈（如视觉、触觉、视觉和触觉）。此外，还改变了伸手任务的物体大小。结果表明，终端触觉反馈对运动学参数有显著影响；与无终端反馈相比，终端触觉反馈显示运动时间缩短了0.7,pm {,1.4}$ s ($p<.05$)，平均速度提高了0.01,pm {,0.08}$ m/s ($p<.05$)。此外，根据物体的大小，在不同的肌肉区域观察到较小的峰值潜伏期。
{"title":"Feedback Type May Change the EMG Pattern and Kinematics During Robot Supported Upper Limb Reaching Task","authors":"Yasuhiro Kato;Toshiaki Tsuji;Imre Cikajlo","doi":"10.1109/OJEMB.2024.3363137","DOIUrl":"https://doi.org/10.1109/OJEMB.2024.3363137","url":null,"abstract":"Haptic interfaces and virtual reality (VR) technology have been increasingly introduced in rehabilitation, facilitating the provision of various feedback and task conditions. However, correspondence between the feedback/task conditions and movement strategy during reaching tasks remains a question. To investigate movement strategy, we assessed velocity parameters and peak latency of electromyography. Ten neuromuscularly intact volunteers participated in the measurement using haptic interface and VR. Concurrent visual feedback and various terminal feedback (e.g., visual, haptic, visual and haptic) were given. Additionally, the object size for the reaching task was changed. The results demonstrated terminal haptic feedback had a significant impact on kinematic parameters; showed \u0000<inline-formula><tex-math>$0.7,pm {,1.4}$</tex-math></inline-formula>\u0000 s (\u0000<inline-formula><tex-math>$p,< .05$</tex-math></inline-formula>\u0000) shorter movement time and \u0000<inline-formula><tex-math>$0.01,pm {,0.08}$</tex-math></inline-formula>\u0000 m/s (\u0000<inline-formula><tex-math>$p,< .05$</tex-math></inline-formula>\u0000) higher mean velocity compared to no terminal feedback. Also, smaller peak latency was observed in different muscle regions based on the object size.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10423822","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139942743","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 : 2024-01-31DOI: 10.1109/OJEMB.2024.3360688
Harry J. Davies;Ghena Hammour;Hongjian Xiao;Patrik Bachtiger;Alexander Larionov;Philip L. Molyneaux;Nicholas S. Peters;Danilo P. Mandic
The rapidly increasing prevalence of debilitating breathing disorders, such as chronic obstructive pulmonary disease (COPD), calls for a meaningful integration of artificial intelligence (AI) into respiratory healthcare. Deep learning techniques are “data hungry” whilst patient-based data is invariably expensive and time consuming to record. To this end, we introduce a novel COPD-simulator, a physical apparatus with an easy to replicate design which enables rapid and effective generation of a wide range of COPD-like data from healthy subjects, for enhanced training of deep learning frameworks. To ensure the faithfulness of our domain-aware COPD surrogates, the generated waveforms are examined through both flow waveforms and photoplethysmography (PPG) waveforms (as a proxy for intrathoracic pressure) in terms of duty cycle, sample entropy, FEV 1 /FVC ratios and flow-volume loops. The proposed simulator operates on healthy subjects and is able to generate FEV 1 /FVC obstruction ratios ranging from greater than 0.8 to less than 0.2, mirroring values that can observed in the full spectrum of real-world COPD. As a final stage of verification, a simple convolutional neural network is trained on surrogate data alone, and is used to accurately detect COPD in real-world patients. When training solely on surrogate data, and testing on real-world data, a comparison of true positive rate against false positive rate yields an area under the curve of 0.75, compared with 0.63 when training solely on real-world data.
{"title":"Physically Meaningful Surrogate Data for COPD","authors":"Harry J. Davies;Ghena Hammour;Hongjian Xiao;Patrik Bachtiger;Alexander Larionov;Philip L. Molyneaux;Nicholas S. Peters;Danilo P. Mandic","doi":"10.1109/OJEMB.2024.3360688","DOIUrl":"https://doi.org/10.1109/OJEMB.2024.3360688","url":null,"abstract":"The rapidly increasing prevalence of debilitating breathing disorders, such as chronic obstructive pulmonary disease (COPD), calls for a meaningful integration of artificial intelligence (AI) into respiratory healthcare. Deep learning techniques are “data hungry” whilst patient-based data is invariably expensive and time consuming to record. To this end, we introduce a novel COPD-simulator, a physical apparatus with an easy to replicate design which enables rapid and effective generation of a wide range of COPD-like data from healthy subjects, for enhanced training of deep learning frameworks. To ensure the faithfulness of our domain-aware COPD surrogates, the generated waveforms are examined through both flow waveforms and photoplethysmography (PPG) waveforms (as a proxy for intrathoracic pressure) in terms of duty cycle, sample entropy, FEV\u0000<sub>1</sub>\u0000/FVC ratios and flow-volume loops. The proposed simulator operates on healthy subjects and is able to generate FEV\u0000<sub>1</sub>\u0000/FVC obstruction ratios ranging from greater than 0.8 to less than 0.2, mirroring values that can observed in the full spectrum of real-world COPD. As a final stage of verification, a simple convolutional neural network is trained on surrogate data alone, and is used to accurately detect COPD in real-world patients. When training solely on surrogate data, and testing on real-world data, a comparison of true positive rate against false positive rate yields an area under the curve of 0.75, compared with 0.63 when training solely on real-world data.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10417113","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139937085","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 : 2024-01-29DOI: 10.1109/OJEMB.2023.3347479
Sofiya Vyshnya;Rachel Epperson;Felipe Giuste;Wenqi Shi;Andrew Hornback;May D. Wang
Objective: To develop a clinical decision support tool that can predict cardiovascular disease (CVD) risk with high accuracy while requiring minimal clinical feature input, thus reducing the time and effort required by clinicians to manually enter data prior to obtaining patient risk assessment. Results: In this study, we propose a robust feature selection approach that identifies five key features strongly associated with CVD risk, which have been found to be consistent across various models. The machine learning model developed using this optimized feature set achieved state-of-the-art results, with an AUROC of 91.30%, sensitivity of 89.01%, and specificity of 85.39%. Furthermore, the insights obtained from explainable artificial intelligence techniques enable medical practitioners to offer personalized interventions by prioritizing patient-specific high-risk factors. Conclusion: Our work illustrates a robust approach to patient risk prediction which minimizes clinical feature requirements while also generating patient-specific insights to facilitate shared decision-making between clinicians and patients.
{"title":"Optimized Clinical Feature Analysis for Improved Cardiovascular Disease Risk Screening","authors":"Sofiya Vyshnya;Rachel Epperson;Felipe Giuste;Wenqi Shi;Andrew Hornback;May D. Wang","doi":"10.1109/OJEMB.2023.3347479","DOIUrl":"10.1109/OJEMB.2023.3347479","url":null,"abstract":"<italic>Objective:</i>\u0000 To develop a clinical decision support tool that can predict cardiovascular disease (CVD) risk with high accuracy while requiring minimal clinical feature input, thus reducing the time and effort required by clinicians to manually enter data prior to obtaining patient risk assessment. \u0000<italic>Results:</i>\u0000 In this study, we propose a robust feature selection approach that identifies five key features strongly associated with CVD risk, which have been found to be consistent across various models. The machine learning model developed using this optimized feature set achieved state-of-the-art results, with an AUROC of 91.30%, sensitivity of 89.01%, and specificity of 85.39%. Furthermore, the insights obtained from explainable artificial intelligence techniques enable medical practitioners to offer personalized interventions by prioritizing patient-specific high-risk factors. \u0000<italic>Conclusion:</i>\u0000 Our work illustrates a robust approach to patient risk prediction which minimizes clinical feature requirements while also generating patient-specific insights to facilitate shared decision-making between clinicians and patients.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10416342","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139949592","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}
Introduction: While carbon ion radiotherapy is highly effective in cancer treatment, it has a high risk of causing soft error, which leads to malfunctions in cardiac implantable electrical devices (CIEDs). To predict the risk of malfunction prior to treatment, it is necessary to measure the reaction cross-sections and contributions to the soft error of secondary particles generated during treatments. Methods: A field-programmable gate array was used instead of CIEDs to measure soft errors by varying the energy spectrum of secondary particles. Results and discussion: The reaction cross-sections measured for each secondary particle were 3.0 × 10 −9 , 2.0 × 10 −9 , 1.3 × 10 −8 , and 1.5 × 10 −8 [cm 2 /Mb] for thermal neutrons, intermediate-energy neutrons, high-energy neutrons above 10 MeV, and protons, respectively. The contribution of high-energy neutrons was the largest among them. Our study indicates that to reduce the risk of soft errors, secure distance and appropriate irradiation directions are necessary.
{"title":"The Contribution of Secondary Particles Following Carbon Ion Radiotherapy to Soft Errors in CIEDs","authors":"Yudai Kawakami;Makoto Sakai;Hiroaki Masuda;Masami Miyajima;Takao Kanzaki;Kazutoshi Kobayashi;Tatsuya Ohno;Hiroshi Sakurai","doi":"10.1109/OJEMB.2024.3358989","DOIUrl":"https://doi.org/10.1109/OJEMB.2024.3358989","url":null,"abstract":"<italic>Introduction:</i>\u0000 While carbon ion radiotherapy is highly effective in cancer treatment, it has a high risk of causing soft error, which leads to malfunctions in cardiac implantable electrical devices (CIEDs). To predict the risk of malfunction prior to treatment, it is necessary to measure the reaction cross-sections and contributions to the soft error of secondary particles generated during treatments. \u0000<italic>Methods:</i>\u0000 A field-programmable gate array was used instead of CIEDs to measure soft errors by varying the energy spectrum of secondary particles. \u0000<italic>Results and discussion:</i>\u0000 The reaction cross-sections measured for each secondary particle were 3.0 × 10\u0000<sup>−9</sup>\u0000, 2.0 × 10\u0000<sup>−9</sup>\u0000, 1.3 × 10\u0000<sup>−8</sup>\u0000, and 1.5 × 10\u0000<sup>−8</sup>\u0000 [cm\u0000<sup>2</sup>\u0000/Mb] for thermal neutrons, intermediate-energy neutrons, high-energy neutrons above 10 MeV, and protons, respectively. The contribution of high-energy neutrons was the largest among them. Our study indicates that to reduce the risk of soft errors, secure distance and appropriate irradiation directions are necessary.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2024-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10415187","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139937065","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 : 2024-01-25DOI: 10.1109/OJEMB.2024.3358562
Giles Hamilton-Fletcher;Mingxin Liu;Diwei Sheng;Chen Feng;Todd E. Hudson;John-Ross Rizzo;Kevin C. Chan
Goal: Distance information is highly requested in assistive smartphone Apps by people who are blind or low vision (PBLV). However, current techniques have not been evaluated systematically for accuracy and usability. Methods: We tested five smartphone-based distance-estimation approaches in the image center and periphery at 1–3 meters, including machine learning (CoreML), infrared grid distortion (IR_self), light detection and ranging (LiDAR_back), and augmented reality room-tracking on the front (ARKit_self) and back-facing cameras (ARKit_back). Results: For accuracy in the image center, all approaches had <±2.5>Conclusions: We provide key information that helps design reliable smartphone-based visual assistive technologies to enhance the functionality of PBLV.
{"title":"Accuracy and Usability of Smartphone-Based Distance Estimation Approaches for Visual Assistive Technology Development","authors":"Giles Hamilton-Fletcher;Mingxin Liu;Diwei Sheng;Chen Feng;Todd E. Hudson;John-Ross Rizzo;Kevin C. Chan","doi":"10.1109/OJEMB.2024.3358562","DOIUrl":"10.1109/OJEMB.2024.3358562","url":null,"abstract":"<italic>Goal:</i>\u0000 Distance information is highly requested in assistive smartphone Apps by people who are blind or low vision (PBLV). However, current techniques have not been evaluated systematically for accuracy and usability. \u0000<italic>Methods:</i>\u0000 We tested five smartphone-based distance-estimation approaches in the image center and periphery at 1–3 meters, including machine learning (CoreML), infrared grid distortion (IR_self), light detection and ranging (LiDAR_back), and augmented reality room-tracking on the front (ARKit_self) and back-facing cameras (ARKit_back). \u0000<italic>Results:</i>\u0000 For accuracy in the image center, all approaches had <±2.5>Conclusions:</i>\u0000 We provide key information that helps design reliable smartphone-based visual assistive technologies to enhance the functionality of PBLV.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10414161","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139949511","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 : 2024-01-24DOI: 10.1109/OJEMB.2024.3355701
Mia Cirrincione;Maren Downing;Kevin Leite;Samuel Dolphin;Adam Samuta;Mack Schermer;Kimberly Noble;Brian Walsh
Background: Over-the-counter (OTC) diagnostic testing is on the rise with many in vitro diagnostic tests being lateral flow assays (LFAs). A growing number of these are adopting reader technologies, which provides an alternative to visual readouts for results interpretation, allowing for improved accessibility of OTC diagnostics. As the reader technology market develops, there are many technologies entering the market, but no clear, single solution has yet been identified. The purpose of this research is to identify and discuss important parameters for the assessment of LFA reader technologies for consideration by manufacturers or researchers. Methods: As part of The National Institute of Biomedical Imaging and Bioengineering's Rapid Acceleration of Diagnostics (RADx) Tech program, reader manufacturers were interviewed to investigate the current state of reader technology development through several parameters identified as important industry standards. Readers were categorized by technology type and parameters including cost, detection method, multiplex capabilities, assay type, maturity, and use case were all assessed. Results: Fifteen reader manufacturers were identified and interviewed, and information on a total of 19 technologies was assessed. Reader technology type was found to be predictive of other attributes, whether the reader is smart technology only, a standalone reader, a reader with smart technology required, or a reader with smart technology optional. Conclusions: Pairing reader technology with OTC diagnostic tests is important for improving existing COVID-19 tests and can be utilized in other diagnostics as the OTC use case grows in popularity. Reader technology type, which is predictive of core reader attributes, should be considered when selecting a reader technology for a specific LFA test within the context of regulatory guidance. As diagnostics increase in complexity, readers provide solutions to accessibility challenges, facilitate public health reporting, and ease the transition to multiplex testing, therefore increasing market availability.
{"title":"Assessment of Reader Technologies for Over-the-Counter Diagnostic Testing","authors":"Mia Cirrincione;Maren Downing;Kevin Leite;Samuel Dolphin;Adam Samuta;Mack Schermer;Kimberly Noble;Brian Walsh","doi":"10.1109/OJEMB.2024.3355701","DOIUrl":"10.1109/OJEMB.2024.3355701","url":null,"abstract":"<bold>Background:</b>\u0000 Over-the-counter (OTC) diagnostic testing is on the rise with many in vitro diagnostic tests being lateral flow assays (LFAs). A growing number of these are adopting reader technologies, which provides an alternative to visual readouts for results interpretation, allowing for improved accessibility of OTC diagnostics. As the reader technology market develops, there are many technologies entering the market, but no clear, single solution has yet been identified. The purpose of this research is to identify and discuss important parameters for the assessment of LFA reader technologies for consideration by manufacturers or researchers. \u0000<bold>Methods:</b>\u0000 As part of The National Institute of Biomedical Imaging and Bioengineering's Rapid Acceleration of Diagnostics (RADx) Tech program, reader manufacturers were interviewed to investigate the current state of reader technology development through several parameters identified as important industry standards. Readers were categorized by technology type and parameters including cost, detection method, multiplex capabilities, assay type, maturity, and use case were all assessed. \u0000<bold>Results:</b>\u0000 Fifteen reader manufacturers were identified and interviewed, and information on a total of 19 technologies was assessed. Reader technology type was found to be predictive of other attributes, whether the reader is smart technology only, a standalone reader, a reader with smart technology required, or a reader with smart technology optional. \u0000<bold>Conclusions:</b>\u0000 Pairing reader technology with OTC diagnostic tests is important for improving existing COVID-19 tests and can be utilized in other diagnostics as the OTC use case grows in popularity. Reader technology type, which is predictive of core reader attributes, should be considered when selecting a reader technology for a specific LFA test within the context of regulatory guidance. As diagnostics increase in complexity, readers provide solutions to accessibility challenges, facilitate public health reporting, and ease the transition to multiplex testing, therefore increasing market availability.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10413534","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139949489","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 : 2024-01-22DOI: 10.1109/OJEMB.2024.3356791
Arne Küderle;Martin Ullrich;Nils Roth;Malte Ollenschläger;Alzhraa A. Ibrahim;Hamid Moradi;Robert Richer;Ann-Kristin Seifer;Matthias Zürl;Raul C. Sîmpetru;Liv Herzer;Dominik Prossel;Felix Kluge;Bjoern M. Eskofier
Goal: Gait analysis using inertial measurement units (IMUs) has emerged as a promising method for monitoring movement disorders. However, the lack of public data and easy-to-use open-source algorithms hinders method comparison and clinical application development. To address these challenges, this publication introduces the gaitmap ecosystem, a comprehensive set of open source Python packages for gait analysis using foot-worn IMUs. Methods: This initial release includes over 20 state-of-the-art algorithms, enables easy access to seven datasets, and provides eight benchmark challenges with reference implementations. Together with its extensive documentation and tooling, it enables rapid development and validation of new algorithm and provides a foundation for novel clinical applications. Conclusion: The published software projects represent a pioneering effort to establish an open-source ecosystem for IMU-based gait analysis. We believe that this work can democratize the access to high-quality algorithm and serve as a driver for open and reproducible research in the field of human gait analysis and beyond.
{"title":"Gaitmap—An Open Ecosystem for IMU-Based Human Gait Analysis and Algorithm Benchmarking","authors":"Arne Küderle;Martin Ullrich;Nils Roth;Malte Ollenschläger;Alzhraa A. Ibrahim;Hamid Moradi;Robert Richer;Ann-Kristin Seifer;Matthias Zürl;Raul C. Sîmpetru;Liv Herzer;Dominik Prossel;Felix Kluge;Bjoern M. Eskofier","doi":"10.1109/OJEMB.2024.3356791","DOIUrl":"https://doi.org/10.1109/OJEMB.2024.3356791","url":null,"abstract":"<italic>Goal:</i>\u0000 Gait analysis using inertial measurement units (IMUs) has emerged as a promising method for monitoring movement disorders. However, the lack of public data and easy-to-use open-source algorithms hinders method comparison and clinical application development. To address these challenges, this publication introduces the gaitmap ecosystem, a comprehensive set of open source Python packages for gait analysis using foot-worn IMUs. \u0000<italic>Methods:</i>\u0000 This initial release includes over 20 state-of-the-art algorithms, enables easy access to seven datasets, and provides eight benchmark challenges with reference implementations. Together with its extensive documentation and tooling, it enables rapid development and validation of new algorithm and provides a foundation for novel clinical applications. \u0000<italic>Conclusion:</i>\u0000 The published software projects represent a pioneering effort to establish an open-source ecosystem for IMU-based gait analysis. We believe that this work can democratize the access to high-quality algorithm and serve as a driver for open and reproducible research in the field of human gait analysis and beyond.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10411039","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139937115","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 : 2024-01-22DOI: 10.1109/OJEMB.2024.3356818
Jason A. Miranda;Adrien Rapeaux;Isabelle C. Samper;Carolina Silveira;David R. Willé;Gerald E. Hunsberger;Wesley J. Dopson;Huanfen Yao;Annapurna Karicherla;Daniel J. Chew
Emerging therapies in bioelectronic medicine highlight the need for deeper understanding of electrode material performance in the context of tissue stimulation. Electrochemical properties are characterized on the benchtop, facilitating standardization across experiments. On-nerve electrochemistry differs from benchtop characterization and the relationship between electrochemical performance and nerve activation thresholds are not commonly established. This relationship is important in understanding differences between electrical stimulation requirements and electrode performance. We report functional electrochemistry as a follow-up to benchtop testing, describing a novel experimental approach for evaluating on-nerve electrochemical performance in the context of nerve activation. An ex-vivo rat sciatic nerve preparation was developed to quantify activation thresholds of fiber subtypes and electrode material charge injection limits for platinum iridium, iridium oxide, titanium nitride and PEDOT. Finally, we address experimental complexities arising in these studies, and demonstrate statistical solutions that support rigorous material performance comparisons for decision making in neural interface development.
{"title":"Functional Electrochemistry: On-Nerve Assessment of Electrode Materials for Electrochemistry and Functional Neurostimulation","authors":"Jason A. Miranda;Adrien Rapeaux;Isabelle C. Samper;Carolina Silveira;David R. Willé;Gerald E. Hunsberger;Wesley J. Dopson;Huanfen Yao;Annapurna Karicherla;Daniel J. Chew","doi":"10.1109/OJEMB.2024.3356818","DOIUrl":"https://doi.org/10.1109/OJEMB.2024.3356818","url":null,"abstract":"Emerging therapies in bioelectronic medicine highlight the need for deeper understanding of electrode material performance in the context of tissue stimulation. Electrochemical properties are characterized on the benchtop, facilitating standardization across experiments. On-nerve electrochemistry differs from benchtop characterization and the relationship between electrochemical performance and nerve activation thresholds are not commonly established. This relationship is important in understanding differences between electrical stimulation requirements and electrode performance. We report functional electrochemistry as a follow-up to benchtop testing, describing a novel experimental approach for evaluating on-nerve electrochemical performance in the context of nerve activation. An ex-vivo rat sciatic nerve preparation was developed to quantify activation thresholds of fiber subtypes and electrode material charge injection limits for platinum iridium, iridium oxide, titanium nitride and PEDOT. Finally, we address experimental complexities arising in these studies, and demonstrate statistical solutions that support rigorous material performance comparisons for decision making in neural interface development.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10411018","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139937108","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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