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Telecommunications Conference (ITC-Egypt), International

作者： Rana M. NourEldeen Rahma A. Elshshtawy Omnia A. Elgohary Fatma Alzahraa A. Wahba Marco K. Attia Zeinab B. Abo elazm Mohamed G. Khattap Hend Galal Eldeen Mohamed Ali Hassan Mariam Mostafa Nayra Ibrahim Reem Ramadan Yomna Refaat Mennatallah Khaled Ali Maher Mohamed Abd Elaziz Ahmed Gamal Abdellatif Ibrahim Radiology and Medical Imaging Faculty of Applied Health Sciences Galala University Suez Egypt Biomedical Informatics Faculty of Computer Science and Engineering Galala University Suez Egypt Artificial Intelligence Science Program Faculty of Computer Science and Engineering Galala University Suez Egypt Department of Electronics and Electrical Communications Engineering Air Defense College Alexandria Egypt

ISBN: (数字)9798350351408

ISBN: (纸本)9798350351415

This paper proposes an alternative detection frame-work for multiple sclerosis (MS) and idiopathic acute transverse myelitis (ATM) within the 6G-enabled Internet of Medical Things (IoMT) environment. The developed framework relies on the implementation of a deep learning technique known as Dense Convolutional Networks (DenseNets) in the 6G-enabled IoMT to enhance prediction performance. To validate the performance of DenseNets, we compared it with other deep learning techniques, including Convolutional Neural Networks (CNN) and MobileNet, using real-world datasets. The experimental results show the high performance of DenseNets in predicting MS and ATM compared to other methods, achieving an accuracy of nearly 90 %.

关键词： 6G mobile communication Deep learning Multiple sclerosis Accuracy Internet of Medical Things Telecommunications Convolutional neural networks

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Constructing Coronary Artery Vessel Segmentation Based on Deep Learning Models and Image Processing 24

Constructing Coronary Artery Vessel Segmentation Based on De...

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8th International Conference on Medical and Health Informatics, ICMHI 2024

作者： Peng, Kang-Syuan Chang, Hsiao-Huang Hsu, Ying-Kai Chien, Ting-Ying Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Cardiovascular Surgery Department Taipei Veterans General Hospital Taipei City TaiwanTaipei Medical University Taipei City Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan City Taiwan TaiwanInnovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan City Taiwan

ISBN: (纸本)9798400716874

Narrowing of the coronary arteries is an important indicator of the severity of coronary artery disease (CAD) in patients. Previous research using deep learning to identify narrowed vessels has primarily been based on object detection, but this approach has several problems, such as the inability to detect multivessel narrowing, calculate the actual rate of narrowing, and determine the exact location of occurrence. Therefore, we believe that the determination of narrowing should return to the original method, namely, calculating the maximum and minimum diameters around the vessel. However, to automate this task, a preprocessing step for separating each vessel is needed, especially for the complex structure of the left coronary artery. We designed a deep learning model capable of analyzing an entire coronary angiography (CAG) image for the separation and tracking of vessels. This not only serves as a preprocessing step for calculating narrowing but also allows for the analysis of different vessel flow directions, providing doctors with comprehensive assessment indicators. © 2024 Copyright ACM.

关键词： Image segmentation

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Seeing in Body Using Wearable Electronics

SSRN

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SSRN 2024年

作者： Linh, Vo Thi Nhat Han, Seunghun Koh, Eunhye Kim, Sumin Jung, Ho Sang Koo, Jahyun Changwon51508 Korea Republic of School of Biomedical Engineering College of Health Science Korea University Seoul02841 Korea Republic of School of Biomedical Engineering Interdisciplinary Program in Precision Public Health Korea University Seoul02841 Korea Republic of Daejeon34113 Korea Republic of Pohang37673 Korea Republic of

Devices used for diagnosing disease are often large, expensive, and require operation by trained professionals, which can result in delayed diagnosis and missed opportunities for timely treatment. However, wearable devices are being recognized as a new approach to overcoming these difficulties, as they are small, affordable, and easy to use. Recent advancements in wearable technology have made monitoring information possible from the surface of organs like the skin and eyes, enabling accurate diagnosis of the user’s internal status. In this review, we categorize the body’s organs into external (e.g., eyes, oral cavity, neck, and skin) and internal (e.g., heart, brain, lung, stomach, and bladder) organ systems and introduce recent developments in the materials and designs of wearable electronics, including electrochemical and electrophysiological sensors applied to each organ system. Further, we discuss recent innovations in wearable electronics for noninvasive monitoring of deep internal organs, such as the heart, brain, and nervous system, using ultrasound, electrical impedance tomography, and temporal interference stimulation. © 2024, The Authors. All rights reserved.

关键词： Wearable technology

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Self-Healable Sandfish Scale-Inspired Scalable Triboelectric Layer for Hybrid Energy Harvesting in Desert Environment

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Small 2024年第4期21卷 e2404637页

作者： Chen, An-Rong Parashar, Parag Sharma, Manish Kumar Shih, Jing-Siang Yeh, Hsuan-Yu Lin, Yen-Jui Kaswan, Kuldeep Fan, Kai-Po Chen, Po-Yu Lin, Zong-Hong Department of Materials Science and Engineering National Tsing Hua University Hsinchu30013 Taiwan Department of Biomedical Engineering National Taiwan University Taipei10617 Taiwan Institute of Biomedical Engineering National Tsing Hua University Hsinchu30013 Taiwan International Intercollegiate Ph.D. Program National Tsing Hua University Hsinchu30013 Taiwan

In deserts, sedimentation from frequent dust activities on solar cells poses a substantial technical challenge, reducing efficiency and necessitating advanced cost-inefficient cleaning mechanisms. Herein, a novel sandfish scale-inspired self-healing fluorinated copolymer-based triboelectric layer is directly incorporated on top of the polysilicon solar cell for sustained hybrid energy harvesting. The transparent biomimetic layer, with distinctive saw-tooth microstructured morphology, exhibits ultra-low sand adhesion and high abrasion-resistant properties, inhibits sedimentation deposition on solar cells, and concurrently harvests kinetic energy from wind-driven sand particles through triboelectric nanogenerator (TENG). The film exhibits a low friction coefficient (0.149), minimal sand adhesion force (27 nN), and a small wear area (327 µm2). In addition, over 2 months, a solar cell with the sandfish scale-inspired structure demonstrates only a 16% decline in maximum power output compared to the bare solar cell, which experiences a 60% decline. Further, the sandfish scale-based TENG device's electrical output is fully restored to its original value after a 6-h self-healing cycle and maintains consistent stable outputs. These results highlight the exceptional advantages of employing biomimetic self-healing materials as robust triboelectric layers, showcasing sustained device stability and durability for prolonged use in harsh desert environments, ultimately contributing to a low cost-of-electricity generation paradigm. © 2024 Wiley-VCH GmbH.

关键词： Nanogenerators

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Enhancing Acute Ischemic Stroke Diagnosis Using IoMT and Deep Learning Technologies

Enhancing Acute Ischemic Stroke Diagnosis Using IoMT and Dee...

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Telecommunications Conference (ITC-Egypt), International

作者： Mohamed M. El-Sheikh Aya Bayoumy Nada M. Ahmed Amany Mohamed Eldeeb Fatma I. Rabea Amira A. Nawar Mohamed G. Khattap Hend Galal Eldeen Mohamed Ali Hassan Mariam Mostafa Nayra Ibrahim Reem Ramadan Yomna Refaat Mennatallah Khaled Ali Maher Mohamed Abd Elaziz Ahmed Gamal Abdellatif Ibrahim Radiology and Medical Imaging Faculty of Applied Health Sciences Galala University Suez Egypt Biomedical Informatics Faculty of Computer Science and Engineering Galala University Suez Egypt Artificial Intelligence Science Program Faculty of Computer Science and Engineering Galala University Suez Egypt Department of Electronics and Electrical Communications Engineering Air Defense College Alexandria Egypt

ISBN: (数字)9798350351408

ISBN: (纸本)9798350351415

This paper introduces an alternative technique for diagnosing Acute Ischemic Stroke within the IoMT environment. In the proposed approach, the collected data is transmitted to a cloud-based center where the technique utilizes EfficientNet, a deep learning model, designed to extract features from MRI images thereby enhancing the detection of acute ischemic infarctions. The performance of EfficientNet is compared against two other models, CNN and MobileNet, demonstrating its superior efficacy through metrics such as accuracy, precision, recall, and F1-score, which stand at 92.31%, 92.28%, 92.33%, and 92.30%, respectively.

关键词： Deep learning Accuracy Magnetic resonance imaging Soft sensors Medical services Feature extraction Regulation

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Improving Mechanical Durability of SLA-Printed Components for Load-Bearing 154th

Improving Mechanical Durability of SLA-Printed Components fo...

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154th Annual Meeting and Exhibition of The Minerals, Metals and Materials Society, TMS 2025

作者： Fani, Niloofar Monfared, Armaghan Hashemi Sadeghzade, Sorour Tavangarian, Fariborz Mechanical Engineering Program School of Science Engineering and Technology Pennsylvania State University Harrisburg MiddletownPA17057 United States Department of Biomedical Engineering Pennsylvania State University State College University ParkPA16802 United States School of Engineering Westlake University Hangzhou310024 China

ISBN: (纸本)9783031807473

This study explored the impact of post-printing parameters in stereolithography (SLA) technology, particularly focusing on how photopolymerization affected the mechanical properties of samples. Building on previous research, the investigation revealed that different curing temperatures significantly influenced the rigidity and stiffness of the printed samples. The mechanical properties of the samples were evaluated through three-point bending tests. Curing at 60 °C left some polymer chains uncured, resulting in less rigid samples but increased load capacity (up to 30 ± 2.4 N) and deflection (2.9 ± 0.6 mm). On the other hand, curing at 70 °C enhanced the rigidity and stiffness, especially for larger samples. The results demonstrated that photopolymerization and the resulting polymer chain cross-linking are crucial for achieving the desired mechanical properties. Notably, as the diameter of the samples decreased, the difference in stiffness between the 60 and 70 °C cured samples also reduced. The findings highlight the importance of optimizing curing temperatures to tailor the mechanical performance of SLA-printed components for specific applications. © The Minerals, Metals & Materials Society 2025.

关键词： Bending tests

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Meta-Learning for Generalizable Connectome Modeling Across Heterogeneous Atlas Spaces

Meta-Learning for Generalizable Connectome Modeling Across H...

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IEEE International Symposium on biomedical Imaging

作者： Qinghao Liang Brendan D. Adkinson Dustin Scheinost Department of Biomedical Engineering Yale University New Haven CT Interdepartmental Neuroscience Program Yale University New Haven CT Department of Radiology and Biomedical Imaging Yale School of Medicine New Haven CT Department of Statistics and Data Science Yale University New Haven CT

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Leveraging large-scale neuroimaging datasets to improve predictive modeling in smaller, specialized cohorts is essential for translating research findings into clinical applications. To address this challenge, we propose a meta-learning inspired approach that enhances phenotype prediction by exploiting inter-phenotype correlations across independent datasets, without requiring uniform imaging data processing. By employing mappings derived from a small, independent cohort with data processed using different atlases, we align the data in the meta-training set to the target space of the meta-testing set. This approach eliminates the need for extensive data reprocessing. Evaluation in the UK Biobank dataset shows that our framework significantly improves predictive performance on unseen phenotypes, demonstrating its potential to generalize connectome-based models to small, heterogeneous cohorts.

关键词： Metalearning Neuroimaging Phenotypes Correlation Biological system modeling Predictive models Data processing biomedical imaging

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Design of an Array Transducer for Localizing Microbubble Activity Through the Skull

Design of an Array Transducer for Localizing Microbubble Act...

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IEEE International Symposium on Applications of Ferroelectrics (ISAF)

作者： Malachy Newman Jiro Kusunose M. Anthony Phipps Allison Dockum Ainsley McDonald-Boyer Charles Caskey Department of Biomedical Engineering Vanderbilt University Nashville USA Department of Radiology Vanderbilt Institute of Imaging Science Vanderbilt University Nashville USA Program in Chemical and Physical Biology Vanderbilt University Nashville USA

ISBN: (数字)9798350371901

ISBN: (纸本)9798350371918

Transcranial focused ultrasound is an important tool for opening the blood brain barrier (BBB), which is typically performed by focusing ultrasonic energy through the skull into regions that have been perfused with microbubbles (MB). When sonicated, these MB oscillate and temporarily disrupt the local BBB. The procedure can be monitored for effectiveness and safety by analyzing the spectral content of echoes, and by using multiple receivers, where the active MB’s position can be triangulated, allowing for the localization of the BBB opening. The monitoring and localization of MB has been performed in rodents but translating into larger animals is a major challenge due to attenuation at the skull. Here, we used acoustic simulations to design a multi-element transmit array with multiple receivers and characterized its ability to generate a focus through the non-human primate (NHP) skull and assessed its performance in localizing MB activity while varying number and spatial arrangement of receivers, optimizing the focal gain, focal spot size, and ability to localize a MB. The resultant transducer allows for the delivery of high pressure at brain-region sized targets and can localize MB signals.

关键词： Location awareness Transducers Ultrasonic imaging Rodents Receivers Acoustics Safety In vitro Monitoring Frequency control

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Evolving Energy Dissipation Characteristics of the Anterior Cruciate Ligament: Digital Image Correlation & Repeated Cycle-Recovery Experiments

SSRN

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SSRN 2025年

作者： Kuetzing, Peter M. Scheven, Ulrich M. Arruda, Ellen M. Department of Mechanical Engineering University of Michigan Ann ArborMI48109 United States Department of Biomedical Engineering University of Michigan Ann ArborMI48109 United States Program in Macromolecular Macromolecular Science and Engineering University of Michigan Ann ArborMI48109 United States

The ability of the Anterior Cruciate Ligament (ACL) to dissipate energy during cyclic loading conditions is crucial for understanding ACL failure. This study employs a novel mechanical test fixture for both shorter duration Digital Image Correlation (DIC) and longer duration Repeated Cycle-Recovery (RCR) ex-vivo experiments. The fixture ensured effective clamping and precise alignment of the Anterior Medial Bone-Ligament-Bone (AM BLB) samples while preserving the integrity and camera view of the region directly above the entheses. Results from the DIC and RCR experiments provide compelling evidence of localized hysteresis and evolving energy dissipation characteristics. The RCR experiments indicate a reduction in the energy absorption capabilities of the AM BLB with progressive cyclic loading, evident by a decrease in normalized hysteresis and diminished viscoelasticity over time. Furthermore, DIC experiments revealed strain concentrations in areas prone to acute ACL injuries. These findings underscore the importance of considering cyclic loading and localized strain behaviors in ACL failure mechanisms and may inform strategies for injury prevention and rehabilitation. © 2025, The Authors. All rights reserved.

关键词： Cyclic loads

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Measuring the Complexity of EMG Signal by Using Fuzzy Approximate Entropy in Post-Stroke Patients Rehabilitation

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Journal of engineering science and Technology Review 2021年第3期14卷 85-90页

作者： Rulaningtyas, Riries Prasetyo, Angga Bagus Rahmatillah, Akif Putra, Alfian Pramudita Rahma, Osmalina Nur Ain, Khusnul Pawana, I Putu Alit Biomedical Engineering Study Program Department of Physics Faculty of Science and Technology Universitas Airlangga Surabaya Indonesia Department of Physical Medicine and Rehabilitation School of Medicine Universitas Airlangga Dr. Soetomo General Academic Hospital Surabaya Indonesia

Stroke is one of world’s highest medical cases that cause physical or mental problem, even death. The treatment for overcoming stroke so far was by conducting rehabilitation or therapy. The therapy progress could be seen over the improvement indicated by stroke patients after participation in rehabilitation program. So far, the assessment of the level of development exhibited by stroke patients are still subjective. Meanwhile, Electromyograph (EMG) can be used as a quantitative method to evaluate the patient's improvement. The complexity of the EMG signal could define the progress of a rehabilitation process related to muscle function. One of the methods to calculate the complexity is fuzzy approximate entropy (fApEn). This study aims to propose fApEn as a quantitative method to evaluate the condition of stroke patients by comparing the complexity of the normal EMG, affected, and unaffected side limb of the patient's EMG signal. The value of fApEn on Subject 1 showed that the affected side obtained the complexity of 0.18141 and on the unaffected side of 0.80740. Subject 2 showed the complexity on the affected side of 0.58597 and the unaffected side of 1.02787, whereas the healthy subjects showed the complexity of 2.07856. The statistical results showed that the data were homogeneous and normally distributed. There was a significant difference between the results of fApEn on the affected side and the unaffected side. Thus, fApEn could be used as a quantitative method to determine the condition of muscle activity. © 2021 School of science, IHU. All rights reserved.

关键词： Entropy

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