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Multimodal SVM Classification for Early-stage Alzheimer's Disease Diagnosis Using T1-weighted MR and F-18 FDG PET Imaging

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Journal of Medical Imaging and Radiation sciences 2024年第3期55卷 101539-101539页

作者： Pamarapa, Mr. Chayanon Keerativittayayut, Dr. Ruedeerat Ekjeen, Dr. Tawatchai Shoombuatong, Dr. Watshara Vichianin, Dr. Yudthaphon School of Radiological Technology Faculty of Health Science Technology Chulabhorn Royal Academy Bangkok Thailand Department of Radiological Technology Faculty of Medical Technology Mahidol University Bangkok Thailand Center of Data mining and Biomedical Informatics Faculty of Medical Technology Mahidol University Bangkok Thailand

Alzheimer's disease is a significant global health challenge characterized by progressive brain degeneration associated with aging. Early detection is crucial for improved prognosis and treatment outcomes. Our study aimed to develop a support vector machine (SVM) classification model using T1-weighted MR and F-18 FDG PET brain imaging to classify cognitive normal (CN) and early-stage Alzheimer's disease, including early mild cognitive impairment (EMCI) and late mild cognitive impairment (LMCI), in individuals aged 65-75. The study comprised three steps. Firstly, image preprocessing involved uniformity processing, B1 bias field correction for MR images, and FWHM optimization with spatial/intensity normalization for PET images. Secondly, MR images were registered to the MNI305 brain template for structural segmentation, and PET images were normalized using the standardized PET template. Co-registration of normalized PET images to segmented MR images provided anatomical segmented PET uptake volumes. These served as input for the classification model. Thirdly, SVM models classified CN vs. LMCI, EMCI vs. LMCI, and CN vs. EMCI using MRI, PET, and combined PET/MR. Feature sets included all features, clinical-based features, and F1-score ranked features, resulting in 27 classification models. In CN vs. EMCI, combined PET/MR with all features achieved 0.71 AUC, 65.78% accuracy, and 68.67% specificity. For CN vs. LMCI, superior performance was observed with combined PET/MR using F1-score ranked features: 0.82 AUC, 77.78% accuracy, and 77.55% specificity. In EMCI vs. LMCI, PET alone with all features achieved the highest performance: 0.72 AUC, 69.23% accuracy, and 60.32% specificity. In conclusion, PET is pivotal in MCI stage differentiation, and using all features aids in challenging tasks (CN vs. EMCI and EMCI vs. LMCI). The combined PET/MR modality notably distinguishes CN from MCI, emphasizing the potential of multimodal imaging to enhance differentiating cognitive states

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Analysing kinematic data from recreational runners using functional data analysis

arXiv

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arXiv 2024年

作者： Gunning, Edward Golovkine, Steven Simpkin, Andrew J. Burke, Aoife Dillon, Sarah Gore, Shane Moran, Kieran O'Connor, Siobhan Whyte, Enda Bargary, Norma Department of Biostatistics Epidemiology and Informatics University of Pennsylvania United States MACSI Department of Mathematics and Statistics University of Limerick Ireland School of Mathematical and Statistical Sciences University of Galway Ireland School of Allied Health Faculty of Education and Health Science University of Limerick Limerick Ireland Centre for Injury Prevention and Performance Athletic Therapy and Training School of Health and Human Performance Dublin City University Dublin Ireland Insight SFI Research Centre for Data Analytics Dublin City University Dublin Ireland

We present a multivariate functional mixed effects model for kinematic data from a large number of recreational runners (N = 288). The runners’ sagittal plane hip and knee angles are modelled jointly as a bivariate function with random effects functions used to account for the dependence among measurements from either side of the body. The model is fitted by first applying multivariate functional principal component analysis (mv-FPCA) and then modelling the mv-FPCA scores using scalar linear mixed effects models. Simulation and bootstrap approaches are introduced to construct simultaneous confidence bands for the fixed effects functions, and covariance functions are reconstructed to summarise the variability structure in the data and thoroughly investigate the suitability of the proposed model. In our scientific application, we observe a statistically significant effect of running speed on both the hip and knee angles. We also observe strong within-subject correlations, reflecting the highly idiosyncratic nature of running technique. Our approach is more generally applicable to modelling multiple streams of smooth kinematic or kinetic data measured repeatedly for multiple subjects in complex experimental designs. © 2024, CC BY.

关键词： Kinematics

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A Multivariate Multilevel Longitudinal Functional Model for Repeatedly Observed Human Movement data

arXiv

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arXiv 2024年

作者： Gunning, Edward Golovkine, Steven Simpkin, Andrew J. Burke, Aoife Dillon, Sarah Gore, Shane Moran, Kieran O’Connor, Siobhan Whyte, Enda Bargary, Norma Department of Biostatistics Epidemiology and Informatics University of Pennsylvania United States MACSI Department of Mathematics and Statistics University of Limerick Ireland School of Mathematical and Statistical Sciences University of Galway Ireland School of Allied Health Faculty of Education and Health Science University of Limerick Limerick Ireland Centre for Injury Prevention and Performance Athletic Therapy and Training School of Health and Human Performance Dublin City University Dublin Ireland Insight SFI Research Centre for Data Analytics Dublin City University Dublin Ireland

Biomechanics and human movement research often involves measuring multiple kinematic or kinetic variables regularly throughout a movement, yielding data that present as smooth, multivariate, time-varying curves and are naturally amenable to functional data analysis. It is now increasingly common to record the same movement repeatedly for each individual, resulting in curves that are serially correlated and can be viewed as longitudinal functional data. In this work, we present a new approach for modelling multivariate multilevel longitudinal functional data, with application to kinematic data from recreational runners collected during a treadmill run. For each stride, the runners’ hip, knee and ankle angles are modelled jointly as smooth multivariate functions that depend on subject-specific covariates. Longitudinally varying multivariate functional random effects are used to capture the dependence among adjacent strides and changes in the multivariate functions over the course of the treadmill run. A basis modelling approach is adopted to fit the model – we represent each observation using a multivariate functional principal components basis and model the basis coefficients using scalar longitudinal mixed effects models. The predicted random effects are used to understand and visualise changes in the multivariate functional data over the course of the treadmill run. In our application, our method quantifies the effects of scalar covariates on the multivariate functional data, revealing a statistically significant effect of running speed at the hip, knee and ankle joints. Analysis of the predicted random effects reveals that individuals’ kinematics are generally stable but certain individuals who exhibit strong changes during the run can also be identified. A simulation study is presented to demonstrate the efficacy of the proposed methodology under realistic data-generating scenarios. © 2024, CC BY.

关键词： Exercise equipment

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Influence of electric field orientation on the effect of ocular current stimulation using full field electroretinogram

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Current Directions in Biomedical Engineering 2021年第2期7卷

作者： Blum, Maren-Christina Leydolph, Lilly Klee, Sascha Institute of Biomedical Engineering and Informatics Faculty of Computer Sciences and Automation Technische Universität Ilmenau Ilmenau Germany Department of General Health Studies Division Biostatistics and Data Science Karl Landsteiner University of Health Sciences Krems an der Donau Austria

Ocular current stimulation (CS) exhibits potential for the treatment of neurodegenerative ocular diseases. For a full field electroretinogram (ffERG) we found no CS effect on the characteristic waves (a-wave, b'-wave, and b-wave). To investigate whether the orientation of the generated electric field has an influence on the CS effect, this study repeated the previous ffERG study with changing one CS electrode position and compared the results of both studies. In the first study 15 (8 m, 27.5 ± 4.5 years) and in the second study 17 (7 m, 22.0 ± 1.9 years) healthy volunteers were stimulated with an anodal, cathodal, and sham direct CS of 800 μA for 5 min in three sessions (randomized, different days). For both studies, a cut-sized ring rubber electrode was placed around the eye. A square rubber electrode was placed for the first study at the ipsilateral temple and for the second study at the visual cortex. Before (ERG 1) and during (ERG 2) the CS, the ffERG was measured. For both studies, the difference between the ERG 1 and ERG 2 measurement (CS effect) for the three characteristic waves was analyzed and compared between the studies. For statistical analysis, the Mann-Whitney U test with Bonferroni correction was applied (α = 0.05). The comparison of the data distribution showed only slight differences between the studies. The Mann-Whitney U test found no significant difference of the CS effect between the studies for all amplitudes and CS groups (p ≥ 0.0055). In the mean, the latency differences were smaller than the time resolution, therefore no statements for latency effects were possible. It can be concluded that the retinal cells generating the ffERG are not affected by ocular CS in either electrode montages tested. © 2021 by Walter de Gruyter Berlin/Boston.

关键词： Rubber

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Lung Ultrasound for the Detection of Pulmonary Tuberculosis Using Expert- and AI-Guided Interpretation: A Prospective Cohort Study

SSRN

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SSRN 2025年

作者： Suttels, Véronique Brokowski, Trevor Wachinou, Ablo Prudence Wolleb, Julia Hada, Aboudou Rasisou Du Toit, Jacques Daniel Fiogbé, Arnauld Attannon Guendehou, Brice Alovokpinhou, Frederic Sefou, Fadyl Makpemikpa, Ginette Bessat, Cécile Roux, Alexia Garcia, Elena Brahier, Thomas Opota, Onya Doenz, Jonathan Vignoud, Julien Agodokpessi, Gildas Affolabi, Dissou Hartley, Mary-Anne Boillat-Blanco, Noémie Department of Medicine Infectious Diseases Lausanne University Hospital University of Lausanne Lausanne Switzerland Yale School of Medicine Department of Biomedical Informatics & Data Science New HavenCT06510 United States Cotonou Benin Faculty of Health Sciences University of the Witwatersrand Johannesburg South Africa Emergency Department Lausanne University Hospital University of Lausanne Lausanne1011 Switzerland Institute of Microbiology University of Lausanne University Hospital Centre Lausanne Switzerland Lausanne1015 Switzerland National Reference Laboratory for Mycobacteriology Cotonou Benin Yale University United States Faculty of Health Sciences Benin Intelligent Global Health Machine Learning and Optimization Laboratory

Background: Point-of-care lung ultrasound (LUS) is a promising tool for portable sputum-free tuberculosis (TB) triage. We investigate the diagnostic performance of LUS to detect TB using expert and artificial intelligence (AI) guided interpretation. We introduce ULTR-AI (Ultrasound-led TB recognition using AI), a suite of deep learning (DL) models designed to automate TB prediction from LUS images. Methods: In this prospective cohort study in a tertiary center in urban Benin, a standardized 14-point sliding scan LUS protocol was performed for symptomatic patients by a trained operator. LUS images were reviewed by two blinded and independent readers. Same-day single sputum Xpert MTB/RIF Ultra® was the microbiological reference standard. The suite comprises three AI models, ULTR-AI, ULTR-AI[signs] and ULTR-AI[max]. ULTR-AI predicts TB directly from images using DL, ULTR-AI[signs] first generates human-recognizable pathological signs before TB risk prediction in a machine learning (ML) model. Finally, ULTR-AI[max] takes the maximal TB risk score predicted by these two models. Findings: Out of 760 patients screened, 504 were analyzed. Median age was 40 (IQR 30-52), 196 (39%) were female, 78 (15%) were people with HIV and 66 (13%) had previous TB. Overall, 192 (38%) had bacteriologically confirmed TB. Human expert interpretation of lung ultrasound achieved a sensitivity of 0·90 (95%CI: 0·89, 0·92), specificity of 0·61 (95%CI: 0·54, 0·67) [AUROC 0·84, 95% CI: 0.82-0.85]. AI-guided interpretation with ULTRA-AI[max] reached a sensitivity of 0·91 (95% CI, 0·90–0·96) and a specificity of 0·85 (95% CI, 0·74–0·88) [AUROC 0·93, 95% 0·92-0·95]. Interpretation: In this cohort, AI-guided LUS meets the WHO requirements for a sputum-free TB triage test, enabling point-of-care testing with minimal infrastructure. ULTR-AI could further help decentralize TB diagnostics in LMICs, improving timely detection. Validation in diverse populations is crucial to confirm clinical utility. © 2025,

关键词： Lung cancer

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Optimal half-metal band structure for large thermoelectric performance

arXiv

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arXiv 2023年

作者： Rahangiar, Finantius E.M. Cahaya, Adam B. Muntini, Melania S. Anshori, Isa Hasdeo, Eddwi H. Department of Physics Universitas Gadjah Mada Sekip Utara BLS 21 Yogyakarta55281 Indonesia Research Center for Quantum Physics National Research and Innovation Agency South Tangerang Indonesia Department of Physics Faculty of Mathematics and Natural Sciences University of Indonesia Depok16424 Indonesia Department of Physics Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Surabaya60111 Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology West Java Bandung Indonesia Bandung Institute of Technology West Java Bandung Indonesia Department of Physics and Material Science University of Luxembourg LuxembourgL-1511 Luxembourg

Half-metal ferromagnets were predicted [in IEEE Trans. Mag. 51, 1 (2015)] to give large thermoelectric performance in anti-parallel spin valve configuration. Despite being metals that suffer from the Wiedemann-Franz law, the additional spin degrees of freedom allow for tuning of the thermoelectric properties due to the spin-valve enhancement factor (SVEF). We test this theory and find a mismatch of parameters that gives large TE performance and large SVEF. As a result, we show that the spin-valve setup is useful only for gapless HMF with initially poor TE performance. To obtain the largest TE performance, one still needs to open the band gap. Copyright © 2023, The Authors. All rights reserved.

关键词： Energy gap

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A Comparative Study of Job-Related Stress in Long-Term Care Workers in Japan and China 9

A Comparative Study of Job-Related Stress in Long-Term Care ...

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9th International Congress on Advanced Applied informatics, IIAI-AAI 2020

作者： Yu, Yating Taki, Seiko Takanokura, Masato Kawakami, Masaru Yamada, Tetsuo Liang, Shuyu Kitami Institute of Technology Regional Management Engineering Course Kitami Japan Chiba Institute of Technology Faculty of Social Systems Science Department of Management Information Science Chiba Japan Kanagawa University Faculty of Engineering Department of Industrial Engineering and Management Yokohama Japan Jichi Medical University Faculty of Nursing Tochigi Japan The University of Electro-Communications Department of Informatics Tokyo Japan Shandong University Data Science Institute Jian China

ISBN: (纸本)9781728173979

Japan revised and implemented the Occupational Safety and Health Act in December 2015 to mandate that businesses with 50 or more workers have all of their workers tested for stress annually. In China, one effect of the 'one child policy' and the nuclearization of the family has been that the number of children has decreased, while the number of elderly people living alone continues to grow. This growth is increasing the demand for long-term care workers. However, because the aging of the Chinese population has been more rapid than in Japan, and China has yet to develop a long-term care system to cope with the aging of its population, the burden on long-term care staff appears to be increasing. Although the resulting need for more support for long-term care staff has become an issue, China has yet to develop a mental health policy like that of Japan that mandates the monitoring of mental health, for example, through annual testing of workers for stress. As a result, the factors underlying long-term care worker stress have yet to be *** study attempted to discover, through a comparison of long-term care worker stress in Japan and China, the kinds of job-related stress care workers tended to have and what the principal underlying factors resulting in high levels of stress may be in each country. To that end, data were collected on workers' psychological and physical stress using the Brief Job Stress Questionnaire (BJSQ) and analyzed to find out how stress may differ by country, gender, job responsibilities, having to work night shifts, or having certain qualifications. © 2020 IEEE.

关键词： Health

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Efficient load profiling and forecasting in large electric power systems 19

Efficient load profiling and forecasting in large electric p...

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19th Conference Information Technologies - Applications and Theory, ITAT 2019

作者： Lendák, Imre Horváth, Tomáš Data Science and Engineering Department Faculty of Informatics Eötvös Loránd University Budapest Hungary

The goal of this paper is to present an efficient load forecasting algorithm for large electric power systems. It uses a combination of nearest neighbor-based load profile clustering and rule-based load forecasting. The load data was sliced into daily load curves, which were K-Means-clustered, thereby compressing data and simplifying the solution. K-Means was chosen in the proof of concept phase and will be substituted with more precise solutions later. In the forecasting phase the daily load profile is predicted based on the forecast date, day type (e.g. weekday or weekend) and historical consumption data for similar days in the past. The solution was tested on a large dataset consisting of one year-long, 5-minute measurement data in a 1900-power-line system. The solution showed excellent performance in both the training and forecast phases. It produced meaningful forecasts even when the input data contained significant amounts of anomalies. An additional advantage of the presented solution is that it can be used for medium and long-term forecasting with limited and/or missing input data. Copyright © 2019 for this paper by its authors.

关键词： Electric power plant loads

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Compatibility of Missing data Handling Methods across the Stages of Producing Clinical Prediction Models

arXiv

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arXiv 2025年

作者： Tsvetanova, Antonia Sperrin, Matthew Jenkins, David A. Peek, Niels Buchan, Iain Hyland, Stephanie Taylor, Marcus Wood, Angela Riley, Richard D. Martin, Glen P. Division of Informatics Imaging and Data Science Faculty of Biology Medicine and Health University of Manchester Manchester Academic Health Science Centre Manchester United Kingdom Department of Public Health and Primary Care University of Cambridge United Kingdom Civic Health Innovation Labs The University of Liverpool Liverpool United Kingdom Microsoft Research Cambridge United Kingdom Department of Cardiothoracic Surgery Manchester University Hospital NHS foundation Trust Manchester United Kingdom British Heart Foundation Cardiovascular Epidemiology Unit Department of Public Health and Primary Care Victor Phillip Dahdaleh Heart and Lung Research Institute University of Cambridge Cambridge United Kingdom Health Data Research UK London United Kingdom Institute of Applied Health Research College of Medical and Dental Sciences University of Birmingham Birmingham United Kingdom

Missing data is a challenge when developing, validating and deploying clinical prediction models (CPMs). Traditionally, decisions concerning missing data handling during CPM development and validation haven’t accounted for whether missingness is allowed at deployment. We hypothesised that the missing data approach used during model development should optimise model performance upon deployment, whilst the approach used during model validation should yield unbiased predictive performance estimates upon deployment – we term this ‘compatibility’. We aimed to determine which combinations of missing data handling methods across the CPM life-cycle are compatible. We considered scenarios where CPMs are intended to be deployed with missing data allowed or not, and we evaluated the impact of that choice on earlier modelling decisions. Through a simulation study and an empirical analysis of thoracic surgery data, we compared CPMs developed and validated using combinations of complete case analysis, mean imputation, single regression imputation, multiple imputation, and pattern sub-modelling. If planning to deploy a CPM without allowing missing data, then development and validation should use multiple imputation when required. Where missingness is allowed at deployment, the same imputation method must be used during development and validation. Commonly used combinations of missing data handling methods result in biased predictive performance estimates. © 2025, CC BY.

关键词： Sorting

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A Secure Federated data-Driven Evolutionary Multi-objective Optimization Algorithm

arXiv

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arXiv 2022年

作者： Liu, Qiqi Yan, Yuping Ligeti, Péter Jin, Yaochu Trustworthy and General AI Lab School of Engineering Westlake University China Faculty of Technology Bielefeld University Bielefeld33619 Germany Faculty of Informatics Department of Computeralgebra Eötvös Loránd University Hungary Smart Data Group E-Group ICT Software Zrt Hungary Department of Computer Science University of Surrey Surrey GuildfordGU2 7XH United Kingdom

data-driven evolutionary algorithms usually aim to exploit the information behind a limited amount of data to perform optimization, which have proved to be successful in solving many complex real-world optimization problems. However, most data-driven evolutionary algorithms are centralized, causing privacy and security concerns. Existing federated Bayesian optimization algorithms and data-driven evolutionary algorithms mainly protect the raw data on each client. To address this issue, this paper proposes a secure federated data-driven evolutionary multiobjective optimization algorithm to protect both the raw data and the newly infilled solutions obtained by optimizing the acquisition function conducted on the server. We select the query points on a randomly selected client at each round of surrogate update by calculating the acquisition function values of the unobserved points on this client, thereby reducing the risk of leaking the information about the solution to be sampled. In addition, since the predicted objective values of each client may contain sensitive information, we mask the objective values with Diffie-Hellman-based noise, and then send only the masked objective values of other clients to the selected client via the server. Since the calculation of the acquisition function also requires both the predicted objective value and the uncertainty of the prediction, the predicted mean objective and uncertainty are normalized to reduce the influence of noise. Experimental results on a set of widely used multi-objective optimization benchmarks show that the proposed algorithm can protect privacy and enhance security with only negligible sacrifice in the performance of federated data-driven evolutionary optimization. Copyright © 2022, The Authors. All rights reserved.

关键词： Evolutionary algorithms

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