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Generalizability of splint therapy for temporomandibular joint disorders in the era of precision medicine

International journal of rheumatic diseases 2023年第1期26卷 170-171页

作者： Shu-Hao Hsu Wei-Wei Liu Yung-Heng Lee Kevin Sheng-Kai Ma School of Medicine Chung Shan Medical University Taichung Taiwan Republic of China. Department of Orthopedics Cishan Hospital Ministry of Health and Welfare Kaohsiung Taiwan Republic of China. Center for Global Health Perelman School of Medicine University of Pennsylvania Philadelphia Pennsylvania USA. Graduate Institute of Biomedical Electronics and Bioinformatics College of Electrical Engineering and Computer Science National Taiwan University Taipei Taiwan Republic of China. Department of Epidemiology Harvard T.H. Chan School of Public Health Boston Massachusetts USA. Department of Dermatology Massachusetts General Hospital Boston Massachusetts USA.

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Respiratory event index underestimates severity of sleep apnea compared to apnea-hypopnea index

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SLEEP Advances 2024年第1期5卷 zpad054页

作者： Pitkänen, Minna Nath, Rajdeep Kumar Korkalainen, Henri Nikkonen, Sami Mahamid, Alaa Oksenberg, Arie Duce, Brett Töyräs, Juha Kainulainen, Samu Leppänen, Timo Department of Technical Physics University of Eastern Finland Kuopio Finland Vtt Technical Research Centre of Finland Ltd Kuopio Finland Diagnostic Imaging Center Kuopio University Hospital Kuopio Finland Sleep Disorders Unit Loewenstein Hospital-Rehabilitation Center Raanana Israel Sleep Disorders Centre Department of Respiratory and Sleep Medicine Princess Alexandra Hospital Brisbane Australia Institute for Health and Biomedical Innovation Queensland University of Technology Brisbane Australia Science Service Center Kuopio University Hospital Kuopio Finland School of Electrical Engineering and Computer Science The University of Queensland Brisbane Australia

Polygraphy (PG) is often used to diagnose obstructive sleep apnea (OSA). However, it does not use electroencephalography, and therefore cannot estimate sleep time or score arousals and related hypopneas. Consequently, the PG-derived respiratory event index (REI) differs from the polysomnography (PSG)-derived apnea-hypopnea index (AHI). In this study, we comprehensively analyzed the differences between AHI and REI. Conventional AHI and REI were calculated based on total sleep time (TST) and total analyzed time (TAT), respectively, from two different PSG datasets (n = 1561). Moreover, TAT-based AHI (AHI_TAT) and TST-based REI (REI_TST) were calculated. These indices were compared keeping AHI as the gold standard. The REI, AHI_TAT, and REI_TST were significantly lower than AHI (p < 0.0001, p ≤ 0.002, and p ≤ 0.01, respectively). The total classification accuracy of OSA severity based on REI was 42.1% and 72.8% for two datasets. Based on AHI_TAT, the accuracies were 68.4% and 85.9%, and based on REI_TST, they were 65.9% and 88.5% compared to AHI. AHI was most correlated with REI_TST (r = 0.98 and r = 0.99 for the datasets) and least with REI (r = 0.92 and r = 0.97). Compared to AHI, REI had the largest mean absolute errors (13.9 and 6.7) and REI_TST the lowest (5.9 and 1.9). REI had the lowest sensitivities (42.1% and 72.8%) and specificities (80.7% and 90.9%) in both datasets. Based on these present results, REI underestimates AHI. Furthermore, these results indicate that arousal-related hypopneas are an important measure for accurately classifying OSA severity. © 2023 The Author(s). Published by Oxford University Press on behalf of Sleep Research Society.

关键词： home testing OSA sleep disordered breathing

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RSI-Net: Two-Stream Deep Neural Network for Remote Sensing Images-based Semantic Segmentation

arXiv

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arXiv 2021年

作者： He, Shuang Lu, Xia Gu, Jason Tang, Haitong Yu, Qin Liu, Kaiyue Ding, Haozhou Chang, Chunqi Wang, Nizhuan Jiangsu Key Laboratory of Marine Bioresources and Environment Jiangsu Key Laboratory of Marine Biotechnology Co-Innovation Center of Jiangsu Marine Bio-Industry Technology Jiangsu Ocean University Lianyungang222005 China School of Geomatics and Marine Information Jiangsu Ocean University Lianyungang222005 China Department of Electrical and Computer Engineering Dalhousie University HalifaxNSB3H 4R2 Canada School of Computer Engineering Jiangsu Ocean University Lianyungang222005 China School of Computer and Software Nanjing University of Information Science and Technology Nanjing211800 China School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen518060 China Peng Cheng Laboratory Shenzhen518055 China School of Biomedical Engineering ShanghaiTech University Shanghai201210 China

For semantic segmentation of remote sensing images (RSI), trade-off between representation power and location accuracy is quite important. How to get the trade-off effectively is an open question, where current approaches of utilizing very deep models result in complex models with large memory consumption. In contrast to previous work that utilizes dilated convolutions or deep models, we propose a novel two-stream deep neural network for semantic segmentation of RSI (RSI-Net) to obtain improved performance through modeling and propagating spatial contextual structure effectively and a decoding scheme with image-level and graph-level combination. The first component explicitly models correlations between adjacent land covers and conduct flexible convolution on arbitrarily irregular image regions by using graph convolutional network, while densely connected atrous convolution network (DenseAtrousCNet) with multi-scale atrous convolution can expand the receptive fields and obtain image global information. Extensive experiments are implemented on the Vaihingen, Potsdam and Gaofen RSI datasets, where the comparison results demonstrate the superior performance of RSI-Net in terms of overall accuracy (91.83%, 93.31% and 93.67% on three datasets, respectively), F1 score (90.3%, 91.49% and 89.35% on three datasets, respectively) and kappa coefficient (89.46%, 90.46% and 90.37% on three datasets, respectively) when compared with six state-of-the-art RSI semantic segmentation methods. © 2021, CC BY-NC-ND.

关键词： Semantic Segmentation

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Recent advances in neurotechnologies with broad potential for neuroscience research (vol 23, pg 1522, 2020)

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NATURE NEUROSCIENCE 2021年第4期24卷 611-611页

作者： Vazquez-Guardado, Abraham Yang, Yiyuan Bandodkar, Amay J. Rogers, John A. Center for Bio-Integrated Electronics Northwestern University Evanston IL USA Department of Materials Science and Engineering Northwestern University Evanston IL USA Department of Mechanical Engineering Northwestern University Evanston IL USA Department of Biomedical Engineering Northwestern University Evanston IL USA Department of Electrical Engineering and Computer Science Northwestern University Evanston IL USA Querrey Simpson Institute for Bioelectronics Northwestern University Chicago IL USA Department of Neurological Surgery Feinberg School of Medicine Northwestern University Chicago IL USA

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Introducing Anisotropic Minkowski Functionals and Quantitative Anisotropy Measures for Local Structure Analysis In biomedical Imaging

arXiv

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arXiv 2020年

作者： Wismüller, Axel De, Titas Lochmüller, Eva Eckstein, Felix Nagarajan, Mahesh B. Departments of Biomedical Engineering & Imaging Sciences University of Rochester United States Department of Electrical & Computer Engineering University of Rochester United States Institute of Anatomy and Musculoskeletal Research Paracelsus Medical University Salzburg Salzburg Austria

The ability of Minkowski Functionals to characterize local structure in different biological tissue types has been demonstrated in a variety of medical image processing tasks. We introduce anisotropic Minkowski Functionals (AMFs) as a novel variant that captures the inherent anisotropy of the underlying gray-level structures. To quantify the anisotropy characterized by our approach, we further introduce a method to compute a quantitative measure motivated by a technique utilized in MR diffusion tensor imaging, namely fractional anisotropy. We showcase the applicability of our method in the research context of characterizing the local structure properties of trabecular bone micro-architecture in the proximal femur as visualized on multi-detector CT. To this end, AMFs were computed locally for each pixel of ROIs extracted from the head, neck and trochanter regions. Fractional anisotropy was then used to quantify the local anisotropy of the trabecular structures found in these ROIs and to compare its distribution in different anatomical regions. Our results suggest a significantly greater concentration of anisotropic trabecular structures in the head and neck regions when compared to the trochanter region (p -4). We also evaluated the ability of such AMFs to predict bone strength in the femoral head of proximal femur specimens obtained from 50 donors. Our results suggest that such AMFs, when used in conjunction with multi-regression models, can outperform more conventional features such as BMD in predicting failure load. We conclude that such anisotropic Minkowski Functionals can capture valuable information regarding directional attributes of local structure, which may be useful in a wide scope of biomedical imaging applications. Copyright © 2020, The Authors. All rights reserved.

关键词： computerized tomography

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On the Emerging Area of Biocybersecurity and Relevant Considerations

arXiv

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arXiv 2020年

作者： Palmer, Xavier-Lewis Potter, Lucas Karahan, Saltuk Biomedical Engineering Institute Department of Electrical and Computer Engineering Old Dominion University NorfolkVA23529 United States Department of Political Science and Geography Old Dominion University NorfolkVA23529 United States

Biocybersecurity is a novel space for the 21st century that meets our innovations in biotechnology and computing head on. Within this space, many considerations are open for and demand consideration as groups endeavor to develop products and policies that adequately ensure asset management and protection. Herein, simplified and brief exploration is given followed by some surface discussion of impacts. These impacts concern the end user, ethical and legal considerations, international proceedings, business, and limitations. It is hoped that this will be helpful in future considerations towards biocybersecurity policy developments and implementations. Copyright © 2020, The Authors. All rights reserved.

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Learning From Alarms: A Robust Learning Approach for Accurate Photoplethysmography-Based Atrial Fibrillation Detection using Eight Million Samples Labeled with Imprecise Arrhythmia Alarms

arXiv

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arXiv 2022年

作者： Ding, Cheng Guo, ZhiCheng Rudin, Cynthia Xiao, Ran Shah, Amit Do, Duc H. Lee, Randall J. Clifford, Gari Nahab, Fadi B. Hu, Xiao Department of Biomedical Engineering Georgia Institute of Technology AtlantaGA United States Department of Electrical and Computer Engineering Duke University DurhamNC United States Department of Computer Science Duke University DurhamNC United States School of Nursing Emory University AtlantaGA United States Dept of Epidemiology RSPH Emory University School of Medicine AtlantaGA United States Department of Medicine Emory University School of Medicine AtlantaGA United States School of Medicine University of California at Los Angeles Los AngelesCA United States School of Medicine University of California at San Francisco San FranciscoCA United States Department of Biomedical Informatics Emory University School of Medicine AtlantaGA United States Department of Neurology Emory University School of Medicine AtlantaGA United States

Atrial fibrillation (AF) is a common cardiac arrhythmia with serious health consequences if not detected and treated early. Detecting AF using wearable devices with photoplethysmography (PPG) sensors and deep neural networks has demonstrated some success using proprietary algorithms in commercial solutions. However, further advancement of this paradigm of continuous AF detection in ambulatory settings, towards a population-wide screening use case, still faces several challenges, one of which is the lack of large-scale labeled training data. To address this challenge, in this study, we propose to leverage AF alarms from bedside patient monitors to label concurrent PPG signals, resulting in the largest PPG-AF dataset so far (8.5M 30-second records from 24100 patients) and demonstrating a practical approach to build large labeled PPG datasets. Furthermore, we recognize that the AF labels thus obtained contain errors because of false AF alarms generated from imperfect built-in algorithms from bedside monitors. Dealing with label noise with unknown distribution characteristics in this case requires advanced algorithms. We, therefore, introduce and open source a novel loss design, the cluster membership consistency (CMC) loss, to mitigate label errors. By comparing CMC with state-of-the-art methods selected from a noisy label competition, we demonstrate its superiority in multiple aspects including handling label noise in PPG data, resilience to poor-quality signals, and computational efficiency. © 2022, CC BY.

关键词： Large dataset

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A Radial Basis Function Technique for the Early Detection and Measurement of Hip Implant Loosening

A Radial Basis Function Technique for the Early Detection an...

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IEEE Sensors Applications Symposium, SAS

作者： Alexander Wiese Ann DiGuglielmo Jerico Mellet Mckayla Rebillon Shreekanth Mandayam Erik Brewer Luke Austin Department of Electrical & Computer Engineering Rowan University Glassboro NJ USA Department of Biomedical Engineering Rowan University Glassboro NJ USA Sidney Kimmel School of Medicine Rothman Orthopedic Institute Philadelphia PA USA

ISBN: (数字)9781728148427

ISBN: (纸本)9781728148434

Hip implants are extremely common procedures that are performed to relieve pain and restore function to the hip joint. Historically, most implants would last for the duration of the patient's life, approximately 15 years after implantation; however, due to medical advancements patients are living longer and outliving the life of their implant. The most common cause for implant failure is the failure of the bond between the implant and the bone, occurring due to the translation or rotation of the implant. Once an implant has failed, a very invasive, costly, and painful revision surgery is required. There is significant advantage in the early detection of implant loosening. This paper presents a radial basis function based image processing technique to detect minute 3-D rotations of a hip implant from 2-D X-ray images. Comparing these rotations for a particular hip implant over time can alert orthopedic surgeons of trends that might lead to impending gross loosening of the implant and enable early correction.

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Deep networks to automatically detect late-activating regions of the heart

arXiv

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arXiv 2020年

作者： Xing, Jiarui Ghadimi, Sona Abdishektaei, Mohammad Bilchick, Kenneth C. Epstein, Frederick H. Zhang, Miaomiao Department of Electrical and Computer Engineering University of Virginia United States Department of Computer Science University of Virginia United States Department of Biomedical Engineering University of Virginia Health System United States School of Medicine University of Virginia Health System United States

This paper presents a novel method to automatically identify late-activating regions of the left ventricle from cine Displacement Encoding with Stimulated Echo (DENSE) MR images. We develop a deep learning framework that identifies late mechanical activation in heart failure patients by detecting the Time to the Onset of circumferential Shortening (TOS). In particular, we build a cascade network performing end-to-end (i) segmentation of the left ventricle to analyze cardiac function, (ii) prediction of TOS based on spatiotemporal circumferential strains computed from displacement maps, and (iii) 3D visualization of delayed activation maps. Our approach results in dramatic savings of manual labors and computational time over traditional optimization-based algorithms. To evaluate the effectiveness of our method, we run tests on cardiac images and compare with recent related works. Experimental results show that the proposed approach provides fast prediction of TOS with improved accuracy. Copyright © 2020, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Rayleigh scattering-based distributed sensing in multicore optical fibers for shape reconstruction in multiplanar disturbance

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Measurement 2025年 256卷

作者： Leandro Macedo Anselmo Frizera Jan Nedoma Radek Martinek Carlos Marques Arnaldo Leal-junior Postgraduate Program in Electrical Engineering of the Federal University of Espírito Santo Av. Fernando Ferrari 514 - Goiabeiras Vitória 29075-910 Espírito Santo Brazil Department of Telecommunications Faculty of Electrical Engineering and Computer Science VSB—Technical University of Ostrava 17. listopadu 15 70833 Ostrava Czech Republic Department of Cybernetics and Biomedical Engineering VSB—Technical University of Ostrava 17. listopadu 15 70833 Ostrava Czech Republic CICECO University of Aveiro Campus Universitário de Santiago 3810-193 Aveiro Portugal

In recent years, advancements in smart cities and multifunctional monitoring have driven new demands for shape reconstruction devices. Multicore fibers (MCFs) are increasingly used due to their parallel data transmission capabilities and multiparameter sensing potential. This paper presents a Rayleigh-scattering-based distributed sensing approach with MCFs for shape reconstruction under multiplanar disturbances. Optical Frequency-Domain Reflectometry (OFDR) is utilized to analyze four cores in a seven-core fiber, enabling shape reconstruction through cross-correlation of spectral responses relative to an unstrained reference. Two configurations are compared: Configuration 1, a spatially separated core arrangement enabling independent strain independent strain measurements to be analyzed with a Frenet-Serret frame modeling and Random Forest (RF) algorithms, achieving a high accuracy with a maximum error of 8.72 × 10 −3 cm; and Configuration 2, a simplified approach analyzing all cores in the same OFDR channel, yielding a higher error of 0.27 cm when a RF algorithm was fed with features derived from the Rayleigh backscattered signal (loss amplitude, strain, spectral shift, and s- and p-polarization) measured by an optical backscatter reflectometer. Three protocols—pure bending, pure torsion, and combined bending/torsion—validate these configurations. The results emphasize this multifunctional MCF-based sensor system’s potential for flexible, integrated shape reconstruction solutions.

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