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biomedical engineering Advances 2025年 10卷

作者： Tania Tahtouh Hadil Salman Nermin Eissa Najla Al Nassar Sofyan Maghaydah Marah Alhalabi Maha Yaghi Abdalla Gad Dana Abdallah Salma Elberry Aysha Alhosani Shaikha Alshehhi Mohammad Alkhedher Mohamad Ramadan Mohammed Ghazal Health Sciences Department Abu Dhabi University Abu Dhabi 59911 United Arab Emirates Electrical Computer and Biomedical Engineering Department Abu Dhabi University Abu Dhabi 59911 United Arab Emirates Department of Mechanical Engineering Abu Dhabi University Abu Dhabi 59911 United Arab Emirates School of Engineering International University of Beirut Beirut 146404 Lebanon

Dietary compliance plays a vital role in the control of chronic diseases and influences response to therapy. The rapid development experienced nowadays through smart technology has enabled the personalization of dietary advice to meet individual needs. This paper provides an overview of the current technological solutions to dietary adherence among patients with chronic conditions. With the increasing prevalence of chronic diseases requiring diet-specific interventions, this review encompasses mobile apps and wearables to IoT-based devices and generative intelligence tools, including chatbots, aimed at offering specific guidance as well as providing individualized support for dietary requirements. This review provides an overview of the strengths and limitations of these approaches in real world applications, as well as emerging methodologies that attempt to improve this by focusing on means to personalize dietary management more effectively. The study suggests that technology-enabled dietary interventions can be useful to support chronic disease management and generative artificial intelligence may have a profound effect on the further personalization of dietary guidance. However, issues surrounding regulatory alignment, content validity and long-term user engagement remain to be resolved in order to maximize the tools’ functionality in clinical and home settings.

关键词： Dietary management Chronic conditions mHealth Personalized health Artificial intelligence Smart technology

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Note on the Existence of Minimizers for Variational Geometric Active Contours

arXiv

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arXiv 2022年

作者： Diop, El Hadji S. Burdin, Valérie Prasath, V.B. Surya Department of Mathematics University of Thies BP 967 Thies Senegal Image and Information Department Institut Mines Telecom - Telecom Bretagne Technopole Brest-Iroise CS 83818 Brest Cedex 329238 France Division of Biomedical Informatics Cincinnati Children’s Hospital Medical Center CincinnatiOH45229 United States The Departments of Biomedical Informatics Pediatrics Electrical Engineering and Computer Science University of Cincinnati OH United States

We propose here a proof of existence of a minimizer of a segmentation functional based on a priori information on target shapes, and formulated with level sets. The existence of a minimizer is very important, because it guarantees the convergence of any numerical methods (either gradient descents techniques and variants, or PDE resolutions) used to solve the segmentation model. This work can also be used in many other segmentation models to prove the existence of a *** Codes 49J40 (Primary), 94A08 (Secondary) © 2022, CC BY.

关键词： Image segmentation

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A review of mri and exercise treatment for improved concussion diagnosis and recovery

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Critical Reviews in biomedical engineering 2020年第5期48卷 261-283页

作者： Danielli, Ethan Dematteo, Carol Hall, Geoffrey B. Noseworthy, Michael D. McMaster School of Biomedical Engineering McMaster University HamiltonON Canada Imaging Research Centre St. Joseph’s Healthcare HamiltonON Canada ARiEAL Research Centre McMaster University HamiltonON Canada Department of Rehabilitation Sciences McMaster University HamiltonON Canada Department of Psychology Neuroscience & Behaviour McMaster University HamiltonON Canada Department of Electrical and Computer Engineering McMaster University HamiltonON Canada Department of Radiology McMaster University HamiltonON Canada Diagnostic Imaging St. Joseph’s Healthcare HamiltonON Canada

Concussions are a major health concern due to the unpredictable onset and resolution of debilitating post-concussion symptoms. This review discusses physiological, structural and functional brain changes post-concus-sion, novel non-invasive medical imaging techniques to improve diagnosis, and the role exercise could play in concussion recovery. After sustaining a concussion, about 50% of youth and 20% of adults have symptoms that last for more than a month. Understanding concussion severity has become consequential in recent years as professional sports leagues have acknowledged their harmful short-and long-term effects. Despite these effects, concussed children and adults continue to return to activity and sport prior to a full recovery. This premature return can be enabled because routine clinical medical imaging techniques are unable to detect post-concussion brain damage. However, there have been advances in MRI approaches that clearly indicate brain damage due to concussion. In terms of recovery, rest has been the long-standing prescribed concussion treatment;however, subsymptom exacerbating exercise has been shown to be a safe and effective treatment option. Novel controlled aerobic exercise interventions have improved concussion outcomes by reducing recovery time and symptom severity. © 2020 by Begell House, Inc.

关键词： Recovery

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Implantable Biosensor for Continuous Serotonin Detection in Freely Moving Crayfish

Implantable Biosensor for Continuous Serotonin Detection in ...

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International Conference on Solid State Sensors and Actuators (TRANSDUCERS)

作者： Jinjing Han Tawen Ho Justin M. Stine Michael A. Straker Jens Herberholz Reza Ghodssi Department of Electrical and Computer Engineering University of Maryland College Park MD USA Institute for System Research University of Maryland College Park MD USA Robert E. Fischell Institute for Biomedical Devices University of Maryland College Park MD USA Department of Psychology University of Maryland College Park MD USA Program in Neuroscience and Cognitive Science University of Maryland College Park MD USA Fischell Department of Bioengineering University of Maryland College Park MD USA

This work presents, for the first time, an implantable biosensor for continuous in vivo serotonin (5-HT) monitoring in freely moving crayfish. 5-HT is an important neurotransmitter that regulates animal behaviors. Conventional methods for detecting 5-HT do not provide real-time information. Our approach facilitates continuous electrochemical monitoring of 5-HT levels without impeding animal natural behavior. The sensing system shows a linear range of 0.4 - 1.2 μΜ with a sensitivity of 0.07 μΑ/μΜ for 5-HT detection. With a 3D-printed package, the sensor can be easily attached to crayfish dorsal carapace allowing electrodes implantation into crayfish heart to monitor neurohormonal dynamics of 5-HT. The implantable biosensor system displayed a significant increase in oxidation currents after 5-HT injections. The device successfully demonstrates the application for in vivo detection of 5-HT in the hemolymph (i.e., blood) of freely behaving crayfish underwater.

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Vibration Analysis in Robot-Driven Glenoid Reaming Procedure

Vibration Analysis in Robot-Driven Glenoid Reaming Procedure

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IEEE/ASME (AIM) International Conference on Advanced Intelligent Mechatronics

作者： Mohammadreza Faieghi S. Farokh Atashzar Mayank Sharma O. Remus Tutunea-Fatan Roy Eagleson Louis M. Ferreira School of Biomedical Engineering Western University Toronto Rehabilitation Institute University Health Network Toronto Ontario Canada Department of Mechanical and Aerospace Engineering Department of Electrical and Computer Engineering New York University USA School of Biomedical Engineering Western University London Ontario Canada Department of Mechanical and Materials Engineering Western University London Ontario Canada Department of Electrical and Computer Engineering Western University London Ontario Canada

ISBN: (数字)9781728167947

ISBN: (纸本)9781728167954

Glenoid reaming is a challenging bone machining operation performed in shoulder joint replacement surgery and represents a suitable target for further developments in haptic surgery simulation. Tool vibration is an important mode of feedback for surgeons to perform glenoid reaming, and thus it is quintessential to be included in virtual replications of this operation. However, there is currently little known about the characteristics of vibrations in glenoid reaming and how it can be reproduced in haptic simulations. To address this, the current study presents an empirical investigation of glenoid reaming vibrations using robot-driven experiments on human cadaveric glenoids. Time- and frequency-domain characteristics of vibration signals obtained from the experiments are analyzed. The power of the vibration signals are predicted as a function of feed force and bone density and the dominant frequencies are identified. The obtained results serve as an important step towards the development of haptic simulators for upper limb orthopedic procedures.

关键词： Bones Surgery Vibrations Robots Haptic interfaces Machining Cancellous bone

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Speckle statistics of biological tissues in optical coherence tomography

arXiv

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arXiv 2021年

作者： Ge, Gary R. Rolland, Jannick P. Parker, Kevin J. The Institute of Optics University of Rochester RochesterNY14627 United States Department of Electrical and Computer Engineering University of Rochester RochesterNY14627 United States Department of Biomedical Engineering University of Rochester NY14627 United States Center for Visual Science University of Rochester NY14627 United States

The speckle statistics of optical coherence tomography images of biological tissue have been studied using several historical probability density functions. A recent hypothesis implies that underlying power-law distributions in the medium structure, such as the fractal branching vasculature, will contribute to power-law probability distributions of speckle statistics. Specifically, these are the Burr type XII distribution for speckle amplitude, the Lomax distribution for intensity, and the generalized logistic distribution for log amplitude. In this study, these three distributions are fitted to histogram data from nine optical coherence tomography scans of various biological tissues and samples. The distributions are also compared with conventional distributions such as the Rayleigh, K, and gamma distributions. The results indicate that these newer distributions based on power laws are, in general, more appropriate models and support the plausibility of their use for characterizing biological tissue. Potentially, the governing power-law parameter of these distributions could be used as a biomarker for tissue disease or pathology. Copyright © 2021, The Authors. All rights reserved.

关键词： Optical tomography

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Real-time ultrasound sensing with a mode-optimized photonic crystal slab

arXiv

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arXiv 2021年

作者： Zhu, Eric Y. Charles-Herrera, Maria Rewcastle, Cory Gad, Raanan Qian, Li Levi, Ofer Department of Electrical and Computer Engineering University of Toronto 10 Kings College Road TorontoONM5S 3G4 Canada Institute of Biomedical Engineering University of Toronto 164 College Street TorontoONM5S 3G9 Canada

Integrated photonic sensors can provide large scale, flexible detection schemes. Photonic crystal slabs (PCS) offer a miniaturized platform for wideband, sensitive ultrasound detection by exploiting the photoelastic effect in water. However, poor modal overlap with the sensing medium and non-negligible absorption loss of the aqueous medium have previously limited PCS sensor performance. In this study, we detail the development and optimization of a PCS-based acoustic sensor, by adding to it a low-loss high-index polymer cladding layer. Exploiting a mode-optimized TM-like optical resonance present in a PCS, with high bulk index sensitivity (>600 nm/RIU) and quality factor Q (>8000), we demonstrate real-time ultrasound-sensing at a noise equivalent pressure (NEP) of 170 Pa (1.9 Pa/√Hz). The PCS sensor is backside-coupled to optical fiber which, along with its intensity-based ultrasound-sensing architecture, will allow us to scale up easily to a 2D array. This work paves the way to a sensitive compact ultrasound detector for photoacoustic-based diagnostics and monitoring of tissue. Copyright © 2021, The Authors. All rights reserved.

关键词： Ultrasonic applications

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Time-dependent elastic numerical model for Optical Coherence Elastography of the murine retina

Time-dependent elastic numerical model for Optical Coherence...

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Bioengineering (ENBENG), Portuguese Meeting in

作者： Carlos Correia Ana Batista Sílvia Barbeiro João Cardoso José P. Domingues Rafael Henriques Custódio Loureiro Mário J. Santos Pedro Serranho Rui Bernardes Miguel Morgado Institute for Biomedical Imaging and Translational Research (CIBIT) University of Coimbra Coimbra Portugal Center of Mathematics of the University of Coimbra (CMUC) Coimbra Portugal Department of Physics Faculty of Science and Technology University of Coimbra Coimbra Portugal Department of Electrical and Computer Engineering Faculty of Science and Technology University of Coimbra Coimbra Portugal Department of Science and Technology Mathematics Section Universidade Aberta Lisbon Portugal Faculty of Medicine Clinical Academic Center of Coimbra (CACC) University of Coimbra Portugal

We present the initial stages of development of a Finite Element Method-based time-dependent elastic numerical model which seeks to support the employment of our Optical Coherence Elastography system for assessing murine retinal elasticity. The current model is able to reconstruct displacement maps in both homogeneous and heterogeneous domains with errors up to a few hundredths relatively to a known exact displacement map, within 1 millisecond. The results demonstrate the robustness of the numerical algorithm under different elastic domains, and model parametrization with real Optical Coherence Elastography data is already in progress.

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AIM-AHEAD Connect: Online Collaboration, Mentoring, and Data Science Training Platform to Increase Researcher Diversity and Advance Health Equity

AIM-AHEAD Connect: Online Collaboration, Mentoring, and Data...

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IEEE International Conference on Healthcare Informatics (ICHI)

作者： Toufeeq Ahmed Aidan Hoyal Katie Stinson Jay Johnson Zainab Latif Legand L. Burge Alexander Libin Guodong Gordon Gao Nawar Shara Jamboor K. Vishwanatha Department of Biomedical Informatics Vanderbilt University Medical Center Nashville United States Department of Electrical Engineering and Computer Science Howard University Washington D.C. United States MedStar Health Research Institute Georgetown University Washington D.C. United States Carey Business School Johns Hopkins University Baltimore United States Institute for Health Disparities University of North Texas Health Science Center at Fort Worth Fort Worth United States

AIM-AHEAD is an NIH-funded consortium whose goal is to advance health equity and researcher diversity via artificial intelligence and machine learning. In our first year of operations, we launched AIM-AHEAD Connect, a centralized platform for the consortium stakeholders containing mentoring, networking, and professional development resources. This paper describes the resources available in AIM-AHEAD Connect as well as collaborative efforts to provide tools and training for the entire consortium.

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Longitudinal Feature Selection and Feature Learning for Parkinson's Disease Diagnosis and Prediction

Longitudinal Feature Selection and Feature Learning for Park...

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International Conference on Pattern Recognition

作者： Zhongwei Huang Haijun Lei Shiqi Li Xiaohua Xiao Ee-Leng Tan Baiying Lei Institute of Computer and Software Shenzhen University Shenzhen China First Affiliated Hospital of Shenzhen University Health Science Center Shenzhen University Shenzhen China School of Electrical & Electronic Engineering Nanyang Technological University Singapore Singapore School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen China

ISBN: (纸本)9781728188089;9781728188096

Parkinson's disease (PD) is an irreversible neurodegenerative disease that seriously affects patients' lives. To provide patients with accurate treatment in time and to reduce deterioration of the disease, it is critical to have an early diagnosis of PD and accurate clinical score predictions. Different from previous studies on PD, most of which only focus on feature selection methods, we propose a network combining joint learning from multiple modalities and relations (JLMMR) with sparse nonnegative autoencoder (SNAE) to further enhance the ability of feature expression. We first preprocess and extract features of the modal neuroimaging data with multiple time points. To extract discriminative and informative features from longitudinal data, we apply JLMMR method for feature selection to avoid over-fitting issues. We further exploit SNAE to learn longitudinal discriminative features for joint disease diagnosis and obtain clinical score predictions. Extensive experiments on the publicly available Parkinson's Progression Markers Initiative (PPMI) dataset show the proposed method produces promising classification and prediction performance, which outperforms state-of-the-art methods as well.

关键词： Neuroimaging Deep learning Parkinson's disease Diversity reception Feature extraction Data models Data mining

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