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The SMART BEAR Project: An Overview of Its Infrastructure 7th

The SMART BEAR Project: An Overview of Its Infrastructure

7th and 8th International Conference on Information and Communication Technologies for Ageing Well and e-Health, ICT4AWE 2021 and ICT4AWE 2022

作者： Su, Qiqi Peretokin, Vadim Basdekis, Ioannis Kouris, Ioannis Maggesi, Jonatan Sicuranza, Mario Acebes, Alberto Bucur, Anca Mukkala, Vinod Jaswanth Roy Pozdniakov, Konstantin Kloukinas, Christos Koutsouris, Dimitrios D. Iliadou, Elefteria Leontsinis, Ioannis Gallo, Luigi De Pietro, Giuseppe Spanoudakis, George Department of Computer Science City University of London London United Kingdom Philips Research Eindhoven Netherlands SPHYNX Technology Solutions AG Zug Switzerland Biomedical Engineering Laboratory School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Computer Science Department Università degli Studi di Milano Milan Italy Institute for High-Performance Computing and Networking National Research Council of Italy ICAR - CNR Naples Italy Atos Research and Innovation Madrid Spain 1st Otolaryngology University Department National and Kapodistrian University of Athens Athens Greece 1st Cardiology Clinic Medical School National and Kapodistrian University of Athens Athens Greece

ISBN: (纸本)9783031374951

The paper describes a cloud-based platform that utilizes Artificial Intelligence (AI) and Explainable AI techniques to deliver evidence-based, personalized interventions to individuals over 65 suffering or at risk of hearing loss, cardiovascular disease, cognitive impairments, balance disorders, or mental health issues, while supporting efficient remote monitoring and clinician-driven guidance. As part of the SMART BEAR integrated project, this platform has been developed to support its large-scale clinical trials. The platform consists of a standards-based data harmonization and management layer, as well as a security component, a Big Data Analytics system, a Clinical Decision Support system, and a dashboard component to facilitate efficient data collection across pilot sites. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Artificial intelligence

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Vision Transformer for Parkinson’s Disease Classification using Multilingual Sustained Vowel Recordings

Vision Transformer for Parkinson’s Disease Classification u...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Daria Hemmerling Marek Wodzinski Juan Rafael Orozco-Arroyave David Sztaho Mateusz Daniol Pawel Jemiolo Magdalena Wojcik-Pedziwiatr Daria Hemmerling Marek Wodzinski Mateusz Daniol and Pawel Jemiolo are With the AGH University of Science and Technology Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering Krakow Poland Information Systems Institute University of Applied Sciences Western Switzerland (HES-SO Valais) Sierre Switzerland Universidad de Antioquia Medellin Colombia and With the Pattern Recognition Lab at the University of Erlangen Erlangen Germany Department of Telecommunications and Media Informatics Budapest University of Technology and Economics Hungary Department of Neurology Andrzej Frycz Modrzewski Krakow University Krakow Poland

Parkinson’s disease (PD) is the 2 nd most prevalent neurodegenerative disease in the world. Thus, the early detection of PD has recently been the subject of several scientific and commercial studies. In this paper, we propose a pipeline using Vision Transformer applied to mel-spectrograms for PD classification using multilingual sustained vowel recordings. Furthermore, our proposed transformed-based model shows a great potential to use voice as a single modality biomarker for automatic PD detection without language restrictions, a wide range of vowels, with an F1-score equal to 0.78. The results of our study fall within the range of the estimated prevalence of voice and speech disorders in Parkinson’s disease, which ranges from 70-90%. Our study demonstrates a high potential for adaptation in clinical decision-making, allowing for increasingly systematic and fast diagnosis of PD with the potential for use in *** relevance— There is an urgent need to develop non invasive biomarker of Parkinson’s disease effective enough to detect the onset of the disease to introduce neuroprotective treatment at the earliest stage possible and to follow the results of that intervention. Voice disorders in PD are very frequent and are expected to be utilized as an early diagnostic biomarker. The voice analysis using deep neural networks open new opportunities to assess neurodegenerative diseases’ symptoms, for fast diagnosis-making, to guide treatment initiation, and risk prediction. The detection accuracy for voice biomarkers according to our method reached close to the maximum achievable value.

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Ultraefficient thermoacoustic conversion through a split ring resonator

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Advanced Photonics 2020年第3期2卷 69-77页

作者： Lu Lan Yueming Li Tiffany Yang-Tran Ying Jiang Yingchun Cao Ji-Xin Cheng Boston University Department of Biomedical EngineeringBostonMassachusettsUnited States Boston University Department of Mechanical EngineeringBostonMassachusettsUnited States Boston University Department of Electrical and Computer EngineeringBostonMassachusettsUnited States Boston University Photonics Center BostonMassachusettsUnited States

Microwaves,which have a∼10-cm wavelength,can penetrate deeper into tissue than photons,heralding exciting deep tissue applications such as modulation or imaging via the thermoacoustic *** conversion efficiency is however very low,even with an exogenous contrast *** break this low-conversion limit,using a split ring resonator to effectively collect and confine the microwaves into a submillimeter hot spot for ultrasound emission and achieve a conversion efficiency over 2000 times higher than other reported thermoacoustic contrast ***,the frequency of emitted ultrasound can be precisely tuned and multiplexed by modulation of the microwave *** performance is inaccessible by a piezoelectric-based transducer or a photoacoustic emitter and,therefore,split ring resonators open up new opportunities to study the frequency response of cells in ultrasonic *** applications in deep tissue localization,a split ring resonator can be used as a wireless,battery-free ultrasound beacon placed under a breast phantom.

关键词： thermoacoustic effect metamaterial photoacoustic effect split ring resonator ultrasound

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FAST ABSOLUTE 3D CGO-BASED electrical IMPEDANCE TOMOGRAPHY ON EXPERIMENTAL TANK DATA

arXiv

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arXiv 2023年

作者： Hamilton, S.J. Muller, P.A. Isaacson, D. Kolehmainen, V. Newell, J. Shishvan, O. Rajabi Saulnier, G. Toivanen, J. The Department of Mathematical and Statistical Sciences Marquette University MilwaukeeWI53233 United States The Department of Mathematics Rensselaer Polytechnic Institute TroyNY12180 United States The Department of Applied Physics University of Eastern Finland KuopioFI-70210 Finland The Department of Mathematics & Statistics Villanova University VillanovaPA19085 United States The Department of Biomedical Engineering Rensselaer Polytechnic Institute TroyNY12180 United States The Department of Electrical and Computer Engineering University at Albany - SUNY AlbanyNY12222 United States

Objective: To present the first 3D CGO-based absolute EIT reconstructions from experimental tank data. Approach: CGO-based methods for absolute EIT imaging are compared to traditional TV regularized non-linear least squares reconstruction methods. Additional robustness testing is performed by considering incorrect modeling of domain shape. Main Results: The CGO-based methods are fast, and show strong robustness to incorrect domain modeling comparable to classic difference EIT imaging and fewer boundary artefacts than the TV regularized non-linear least squares reference reconstructions. Significance: This work is the first to demonstrate fully 3D CGO-based absolute EIT reconstruction on experimental data and also compares to TV-regularized absolute reconstruction. The speed (1-5 seconds) and quality of the reconstructions is encouraging for future work in absolute EIT. © 2023, CC BY.

关键词： Image reconstruction

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AI-enabled ultra-low-dose CT reconstruction

arXiv

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arXiv 2021年

作者： Wu, Weiwen Niu, Chuang Ebrahimian, Shadi Yu, Hengyong Kalra, Mannu Wang, Ge Biomedical Imaging Center Center for Biotechnology and Interdisciplinary Studies Department of Biomedical Engineering Rensselaer Polytechnic Institute TroyNY United States Department of Radiology Massachusetts General Hospital Harvard Medical School BostonMA United States Department of Electrical & Computer Engineering University of Massachusetts Lowell LowellMA United States

By the ALARA (As Low As Reasonably Achievable) principle, ultra-low-dose CT reconstruction is a holy grail to minimize cancer risks and genetic damages, especially for children. With the development of medical CT technologies, the iterative algorithms are widely used to reconstruct decent CT images from a low-dose scan. Recently, artificial intelligence (AI) techniques have shown a great promise in further reducing CT radiation dose to the next level. In this paper, we demonstrate that AI-powered CT reconstruction offers diagnostic image quality at an ultra-low-dose level comparable to that of radiography. Specifically, here we develop a Split Unrolled Grid-like Alternative Reconstruction (SUGAR) network, in which deep learning, physical modeling and image prior are integrated. The reconstruction results from clinical datasets show that excellent images can be reconstructed using SUGAR from 36 projections. This approach has a potential to change future *** Codes 68T07 Copyright © 2021, The Authors. All rights reserved.

关键词： Deep learning

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A dataset and benchmark for hospital course summarization with adapted large language models

arXiv

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arXiv 2024年

作者： Aali, Asad Van Veen, Dave Arefeen, Yamin Ishraq Hom, Jason Bluethgen, Christian Reis, Eduardo Pontes Gatidis, Sergios Clifford, Namuun Daws, Joseph Tehrani, Arash S. Kim, Jangwon Chaudhari, Akshay S. Department of Radiology Stanford University StanfordCA United States Department of Electrical and Computer Engineering The University of Texas at Austin AustinTX United States Center for Artificial Intelligence in Medicine and Imaging Stanford University Palo AltoCA United States Department of Electrical Engineering Stanford University StanfordCA United States Department of Medicine StanfordCA United States University Hospital Zurich Zurich Switzerland Albert Einstein Israelite Hospital São Paulo Brazil School of Nursing The University of Texas at Austin AustinTX United States One Medical San FranciscoCA United States Amazon SeattleWA United States Department of Biomedical Data Science Stanford University StanfordCA United States

Objective: Brief hospital course (BHC) summaries are clinical documents that summarize a patient’s hospital stay. While large language models (LLMs) depict remarkable capabilities in automating real-world tasks, their capabilities for healthcare applications such as synthesizing BHCs from clinical notes have not been shown. We introduce a novel pre-processed dataset, the MIMIC-IV-BHC, encapsulating clinical note and brief hospital course (BHC) pairs to adapt LLMs for BHC synthesis. Furthermore, we introduce a benchmark of the summarization performance of two general-purpose LLMs and three healthcare-adapted LLMs. Materials and Methods: Using clinical notes as input, we apply prompting-based (using in-context learning) and fine-tuning-based adaptation strategies to three open-source LLMs (Clinical-T5-Large, Llama2-13B, FLAN-UL2) and two proprietary LLMs (GPT-3.5, GPT-4). We evaluate these LLMs across multiple context-length inputs using natural language similarity metrics. We further conduct a clinical study with five clinicians, comparing clinician-written and LLM-generated BHCs across 30 samples, focusing on their potential to enhance clinical decision-making through improved summary quality. We compare reader preferences for the original and LLM-generated summary using Wilcoxon Signed-Rank tests. We further request optional qualitative feedback from clinicians to gain deeper insights into their preferences, and we present the frequency of common themes arising from these comments. Results: The Llama2-13B fine-tuned LLM outperforms other domain-adapted models given quantitative evaluation metrics of BLEU and BERT-Score. GPT-4 with in-context learning shows more robustness to increasing context lengths of clinical note inputs than fine-tuned Llama2-13B. Despite comparable quantitative metrics, the reader study depicts a significant preference for summaries generated by GPT-4 with in-context learning compared to both Llama2-13B fine-tuned summaries and the original s

关键词： Electronic health record

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SSD-KD: A Self-supervised Diverse Knowledge Distillation Method for Lightweight Skin Lesion Classification Using Dermoscopic Images

arXiv

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arXiv 2022年

作者： Wang, Yongwei Wang, Yuheng Lee, Tim K. Miao, Chunyan Wang, Z. Jane Joint NTU-WeBank Research Center on Fintech Nanyang Technological University Singapore Singapore Department of Electrical and Computer Engineering University of British Columbia VancouverBC Canada School of Biomedical Engineering University of British Columbia VancouverBC Canada Department of Dermatology and Skin Science University of British Columbia VancouverBC Canada Photomedicine Institute Vancouver Coast Health Research Institute VancouverBC Canada Cancer Control Research Program Bc Cancer VancouverBC Canada School of Computer Science and Engineering Nanyang Technological University Singapore Singapore

Skin cancer is one of the most common types of malignancy, affecting a large population and causing a heavy economic burdenworldwide. Over the last fewyears, computer-aided diagnosis has been rapidly developed and make great progress in healthcare and medical practices due to the advances in artificial intelligence, particularly with the adoption of convolutional neural networks. However, most studies in skin cancer detection keep pursuing high prediction accuracies without considering the limitation of computing resources on portable devices. In this case, the knowledge distillation (KD) method has been proven as an efficient tool to help improve the adaptability of lightweight models under limited resources, meanwhile keeping a high-level representation capability. To bridge the gap, this study specifically proposes a novel method, termed SSD-KD, that unifies diverse knowledge into a generic KD framework for skin diseases classification. Our method models an intra-instance relational feature representation and integrates it with existing KD research. A dual relational knowledge distillation architecture is self-supervisedly trained while the weighted softened outputs are also exploited to enable the student model to capture richer knowledge from the teacher model. To demonstrate the effectiveness of our method, we conduct experiments on ISIC 2019, a large-scale open-accessed benchmark of skin diseases dermoscopic images. Experiments show that our distilled lightweight model can achieve an accuracy as high as 85% for the classification tasks of 8 different skin diseases with minimal parameters and computing requirements. Ablation studies confirm the effectiveness of our intra- and inter-instance relational knowledge integration strategy. Compared with state-of-the-art knowledge distillation techniques, the proposed method demonstrates improved performances. To the best of our knowledge, this is the first deep knowledge distillation application for multi-diseases cl

关键词： Dermatology

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Combining multiple contrasts for improving machine learning-based classification of cervical cancers with a low-cost point-of-care Pocket colposcope

Combining multiple contrasts for improving machine learning-...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Mercy N. Asiedu Erica Skerrett Guillermo Sapiro Nirmala Ramanujam Department of Biomedical Engineering and Department of Global Health Duke University Durham NC USA Department of Biomedical Engineering and Department of Electrical Engineering and Computer Science Duke University Durham NC USA

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

We apply feature-extraction and machine learning methods to multiple sources of contrast (acetic acid, Lugol's iodine and green light) from the white Pocket Colposcope, a low-cost point of care colposcope for cervical cancer screening. We combine features from the sources of contrast and analyze diagnostic improvements with addition of each contrast. We find that overall AUC increases with additional contrast agents compared to using only one source.

关键词： Feature extraction Classification algorithms Image segmentation Machine learning Cervical cancer Image color analysis

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Meaningfully Debugging Model Mistakes using Conceptual Counterfactual Explanations

arXiv

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arXiv 2021年

作者： Abid, Abubakar Yuksekgonul, Mert Zou, James Department of Electrical Engineering Stanford University United States Department of Computer Science Stanford University United States Department of Biomedical Data Science Stanford University StanfordCA94305 United States

Understanding and explaining the mistakes made by trained models is critical to many machine learning objectives, such as improving robustness, addressing concept drift, and mitigating biases. However, this is often an ad hoc process that involves manually looking at the model's mistakes on many test samples and guessing at the underlying reasons for those incorrect predictions. In this paper, we propose a systematic approach, conceptual counterfactual explanations (CCE), that explains why a classifier makes a mistake on a particular test sample(s) in terms of human-understandable concepts (e.g. this zebra is misclassified as a dog because of faint stripes). We base CCE on two prior ideas: counterfactual explanations and concept activation vectors, and validate our approach on well-known pretrained models, showing that it explains the models' mistakes meaningfully. In addition, for new models trained on data with spurious correlations, CCE accurately identifies the spurious correlation as the cause of model mistakes from a single misclassified test sample. On two challenging medical applications, CCE generated useful insights, confirmed by clinicians, into biases and mistakes the model makes in real-world settings. The code for CCE is publicly available at https://***/mertyg/debug-mistakes-cce. © 2021, CC BY.

关键词： Medical applications

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Muscle coactivation analysis for neuromuscular control assessment of lower limb stretch-shortening cycle

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Gait & Posture 2023年 106卷 S175-S176页

作者： Rodrigues, Carlos Correia, Miguel Abrantes, João Benedetti, Marco Nadal, Jurandir Faculty of Engineering- University of Porto Biomedical Engineering Program Porto Portugal INESC TEC - Institute for Systems and Computer Engineering- Technology and Science C-BER - Centre for Biomedical Engineering Research Porto Portugal Faculty of Engineering- University of Porto Department of Electrical and Computer Engineering Porto Portugal CICANT - Centre for Research in Applied Communication- Culture and New Technologies MovLab - Laboratory of Technologies for Interactions and Interfaces Lisbon Portugal Federal University of Pernambuco Department of Electronic and Systems Recife Brazil Federal University of Rio de Janeiro Biomedical Engineering Program Rio de Janeiro Brazil

IntroductionMuscle stretch-shortening cycle (SSC) is a central mechanism with lower limb muscle contraction immediately preceded by muscle stretch for efficient submaximal activities such as gait and powerful maximal activities such as running and jumping [1,2]. Research questionAlthough muscle SSC can be observed at gait and running its higher expression and accessibility is performed on standard maximum vertical jump (MVJ) with an open issue on neuromuscular control assessment of lower limb muscle SSC [3]. MethodsFor this purpose, we present and applied noninvasive subject specific analysis of lower limb muscle coactivation for selected muscles with higher contribution during MVJ impulse based on corresponding conditioned surface electromyographic signals (sEMG). Selected muscles correspond to lower limb muscles vastus medialis (VM), rectus femoris (RF), vastus lateralis (VL), lateral gastrocnemius (LG) and medial gastrocnemius (MG). Twenty-seven trials were assessed corresponding for each subject to the highest MVJ based on larger flight time with long SSC at countermovement jump (CMJ), short SSC at drop jump (DJ) and squat jump (SJ) with no SSC. Trial sample is composed by a group of six young adult volunteers’ students on sports and physical education degree with the ages (21.5 ± 1.4) years, (76.7 ± 9.3) kg mass and (1.79 ± 0.06) m height. Surface skin was prepared and Aqua-Wet gel Skintact F55 electrodes were applied at bipolar configuration as indicated by SENIAN. VM, RF, VL, LG and MG sEMG linear envelopes were paired plotted with the area under the curves computed as well as the coactivation pairs defined as the common area under the curves normalized to the sum of the corresponding pair individual areas under the curves. Coactivations were compared at CMJ, DJ and SJ as well as among CMJ, DJ and SJ, Fig. 1. ResultsStrongest coactivations with decreasing intensity were detected between VM-RF and LG-MG muscles at CMJ and SJ, whereas at DJ the strongest coacti

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