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Full-field electroretinography examinations of the human eye with the eye diagnostic device PEP-2000-First results

Current Directions in biomedical Engineering 2022年第2期8卷 636-639页

作者： Schieding, Nikola Reuter, Thomas Grundmann, Andreas Walther, Sebastian Klee, Sascha ICM-Institut Chemnitzer Maschinen-und Anlagenbau E.V. Otto-Schmerbach-Str. 19 Chemnitz 09117 Germany Department of General Health Studies Division Biostatistics and Data Science Karl Landsteiner University of Health Sciences Krems an der Donau Austria Institute of Biomedical Engineering and Informatics Faculty of Computer Sciences and Automation TU Ilmenau Ilmenau Germany

Full-field electroretinography (full-field ERG) forms the diagnostic basis for numerous pathologies of the eye. For this reason, fast and accurate diagnostics in the field of ophthalmology are essential. Two examination techniques, full-field ERG and pupillometry were combined in a diagnostic device developed by ICM e.V. to reduce the examination process for both examiners and patients. In this paper, the device is examined for the quality of the full-field ERG measurements. A feasibility study with 12 healthy subjects (3 f, 9 m, 36.33 ± 11.94 years) was conducted to evaluate the device. The results showed that the peak times for both light-and dark-adapted measurements were within the range of the researched literature values. However, the amplitudes were markedly lower in both measurements compared to the averaged literature values (dark-adapted about 8.5-fold and light-adapted about 5.5-fold) and are clearly outside the range of values researched. The main reason for this is the use of cup electrodes, which were placed on the skin of the lower eyelid. Nevertheless, plausible and comparable analysis values could be obtained with the eye diagnostic device PEP-2000. Further studies with wire electrodes will be performed. © 2022 by Walter de Gruyter Berlin/Boston.

关键词： Ophthalmology

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Multitask classification: assessing data complexity and determining correlations with classifier performance

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Neural Computing and Applications 2024年 1-18页

作者： Villuendas-Rey, Yenny Taylor, R. Andrew Centro de Innovación y Desarrollo Tecnológico en Cómputo Instituto Politécnico Nacional Juan de Dios Bátiz sn GAM Mexico City07700 Mexico Department of Biomedical Informatics and Data Science Yale School of Medicine 100 College Street New HavenCT06520 United States Department of Emergency Medicine Yale School Medicine 464 Congress Ave. New HavenCT06519 United States Yale School of Public Health Yale University 60 College Street New HavenCT06520-0834 United States

This paper introduces significant advancements in addressing data complexity within multitask classification problems. It presents 11 novel multitask data complexity measures adept at handling the intricacies of hybrid datasets and coping with missing values. Moreover, it introduces the Multitask Customized Naïve Associative Classifier (MCNAC), a pioneering instance-based multitask supervised classifier crafted through algorithmic adaptation. We further assess the complexity of 23 diverse datasets, revealing substantial correlations between eight of the proposed data complexity measures and the performance metrics of the novel classifier. These findings underscore the practical utility of these metrics in effectively predicting classifier performance across varied dataset scenarios. Furthermore, the MCNAC emerges as a promising solution, demonstrating its versatility in accommodating hybrid and incomplete data while retaining interpretability. In addition, we used an automatic attribute weighting strategy for MCNAC based on differential evolution. The experimental analysis shows the good performance of MCNAC in comparison with state-of-the-art multitask classifiers. In summary, this research highlights the critical significance of meticulously evaluating data complexity in multitask supervised classification tasks. By introducing a comprehensive suite of measures and innovative classifiers precisely engineered to address these complexities, our study contributes significantly to the enhancement of performance and interpretability in multitask classification contexts, thereby enriching the landscape of machine learning methodologies. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： data assimilation

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Formation Tracking of Mobile Robots Using Only Constrained and Faulty Visual Feedback

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IEEE Transactions on Vehicular Technology 2025年

作者： Guan, Renhe Liu, Xiaofeng Wang, Wei Wang, Yan Harbin Institute of Technology Shenzhen School of Intelligence Science and Engineering Shenzhen China Yale University Departments of Radiology & Biomedical Imaging Biomedical Informatics & Data Science New HavenCT06519 United States Xi'an Jiaotong University School of Management No.28 Xianning West Road Xi'an China

This paper investigates the leader-follower formation tracking problem for nonholonomic mobile robots equipped solely with an RGB-D camera, eliminating the need for communication devices, which significantly reduces system complexity and costs. However, achieving robust tracking with this minimal configuration is highly challenging, as the follower's camera often fails to detect the leader due to two key issues that are often overlooked in existing research. First, the limited field-of-view (FOV) of the camera frequently allows the leader to move out of sight. Second, visual detection faults—including obstacle occlusion, lighting variations, depth image failures, and false detections caused by similar objects—complicate reliable tracking. To address these challenges, we propose a novel visual formation tracking scheme. Our approach allows the follower robot to maintain continuous tracking of the leader within its constrained FOV in the absence of visual faults, even without access to the leader's depth or velocity. When visual faults occur, the system swiftly diagnoses the fault type and resumes tracking accordingly. Our method leverages formation kinematics to enhance efficiency and reduce computation while requiring only an onboard camera, and its effectiveness is validated through simulations and real-world experiments. © 1967-2012 IEEE.

关键词： Mobile robots

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powerROC: An Interactive Web Tool for Sample Size Calculation in Assessing Models' Discriminative Abilities

arXiv

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arXiv 2025年

作者： Grolleau, François Tibshirani, Robert Chen, Jonathan H. Stanford Center for Biomedical Informatics Research Stanford University United States Department of Statistics Stanford University United States Department of Biomedical Data Science Stanford University United States Stanford Clinical Excellence Research Center Stanford University United States Division of Hospital Medicine Stanford University United States

Rigorous external validation is crucial for assessing the generalizability of prediction models, particularly by evaluating their discrimination (AUROC) on new data. This often involves comparing a new model's AUROC to that of an established reference model. However, many studies rely on arbitrary rules of thumb for sample size calculations, often resulting in underpowered analyses and unreliable conclusions. This paper reviews crucial concepts for accurate sample size determination in AUROC-based external validation studies, making the theory and practice more accessible to researchers and clinicians. We introduce powerROC, an open-source web tool designed to simplify these calculations, enabling both the evaluation of a single model and the comparison of two models. The tool offers guidance on selecting target precision levels and employs flexible approaches, leveraging either pilot data or user-defined probability distributions. We illustrate powerROC's utility through a case study on hospital mortality prediction using the MIMIC database. Copyright © 2025, The Authors. All rights reserved.

关键词： Prediction models

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A deep learning fusion model for accurate classification of brain tumours in Magnetic Resonance images

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 790-804页

作者： Nechirvan Asaad Zebari Chira Nadheef Mohammed Dilovan Asaad Zebari Mazin Abed Mohammed Diyar Qader Zeebaree Haydar Abdulameer Marhoon Karrar Hameed Abdulkareem Seifedine Kadry Wattana Viriyasitavat Jan Nedoma Radek Martinek Department of Information Technology Lebanese French UniversityErbilIraq Department of Computer Science University of ZakhoZakhoKurdistan RegionIraq Department of Computer Science College of ScienceNawroz UniversityDuhokKurdistan RegionIraq Department of Artificial Intelligence College of Computer Science and Information TechnologyUniversity of AnbarRamadiIraq Department of Telecommunications VSB‐Technical University of OstravaOstravaCzech Republic Department of Cybernetics and Biomedical Engineering VSB‐Technical University of OstravaOstravaCzech Republic Department of Computer Network and Information Security Technical College of Informatics-AkreDuhok Polytechnic UniversityDuhokIraq Information and Communication Technology Research Group Scientific Research CenterAl‐Ayen UniversityThi‐QarIraq College of Computer Sciences and Information Technology University of KerbalaKarbalaIraq College of Agriculture Al‐Muthanna UniversitySamawahIraq Department of Applied Data Science Noroff University CollegeKristiansandNorway Faculty of Commerce and Accountancy Chulalongkorn Business SchoolChulalongkorn UniversityBangkokThailand

Detecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods still need to solve this problem despite the numerous available approaches. Precise analysis of Magnetic Resonance Imaging (MRI) is crucial for detecting, segmenting, and classifying brain tumours in medical diagnostics. Magnetic Resonance Imaging is a vital component in medical diagnosis, and it requires precise, efficient, careful, efficient, and reliable image analysis techniques. The authors developed a Deep Learning (DL) fusion model to classify brain tumours reliably. Deep Learning models require large amounts of training data to achieve good results, so the researchers utilised data augmentation techniques to increase the dataset size for training models. VGG16, ResNet50, and convolutional deep belief networks networks extracted deep features from MRI images. Softmax was used as the classifier, and the training set was supplemented with intentionally created MRI images of brain tumours in addition to the genuine ones. The features of two DL models were combined in the proposed model to generate a fusion model, which significantly increased classification accuracy. An openly accessible dataset from the internet was used to test the model's performance, and the experimental results showed that the proposed fusion model achieved a classification accuracy of 98.98%. Finally, the results were compared with existing methods, and the proposed model outperformed them significantly.

关键词： brain tumour deep learning feature fusion model MRI images multi‐classification

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Temporal Ensemble Logic

arXiv

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arXiv 2024年

作者： Zhang, Guo-Qiang Department of Neurology McGovern Medical School Department of Health Data Science and Artificial Intelligence McWilliams School of Biomedical Informatics Texas Institute for Restorative Neurotechnologies The University of Texas Health Science Center at Houston 1133 John Freemen Ave HoustonTX77030 United States

We introduce Temporal Ensemble Logic (TEL), a monadic, first-order modal logic for linear-time temporal reasoning. TEL includes primitive temporal constructs such as "always up to t time later" (t), "sometimes before t time in the future" (♢t), and "t-time later" φt. TEL has been motivated from the requirement for rigor and reproducibility for cohort specification and discovery in clinical and population health research, to fill a gap in formalizing temporal reasoning in biomedicine. Existing logical frameworks such as linear temporal logic are too restrictive to express temporal and sequential properties in biomedicine, or too permissive in semantic constructs, such as in Halpern-Shoham logic, to serve this purpose. In this paper, we first introduce TEL in a general set up, with discrete and dense time as special cases. We then focus on the theoretical development of discrete TEL on the temporal domain of positive integers N+, denoted as TELN+. TELN+ is strictly more expressive than the standard monadic second order logic, characterized by Büchi automata. We present its formal semantics, a proof system, and provide a proof for the undecidability of the satisfiability of TELN+. We also include initial results on expressiveness and decidability fragments for TELN+, followed by application outlook and discussions. Copyright © 2024, The Authors. All rights reserved.

关键词： Clinical research

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Are dogs not susceptible to retroviral infections?

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Animal Diseases 2024年第3期4卷 217-223页

作者： Jorge Casseb João Henrique Campos Luciano Rodrigo Lopes Laboratory of Investigation in Dermatology and Immunodefciencies Department of DermatologyUniversity of São Paulo Medical SchoolSão PauloSPBrazil Igen-Institute of Immunogenetics São PauloSPBrazil Bioinformatics and Biomedical Data Science Division Health Informatics DepartmentUniversidade Federal de São Paulo–UNIFESP.Rua Botucatu 862Prédio Leal PradotérreoVila ClementinoCEP 04023-062São PauloSPBrazil

Retroviruses have been proven to cause infections and diseases in a series of mammalian hosts but not in ***,this letter discussed the dog susceptibility to retrovirus infection,encompassing arguments to understand why dogs may have not been infected by retroviruses thus *** potential resistance of retrovirus in dogs enables this provocative short communication to discuss this question,looking at some evolutive *** lineage of canids has shown,throughout its evolutionary history,a smaller accumulation of retroviruses in canid genomes,classifed as endogenous *** this context,the genomes of canids seem to ofer obstacles,which have been evolutionarily conserved,in the face of retroviral infection.

关键词： Retrovirus dogs Endogenous retrovirus

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Cycle Invariant Positional Encoding for Graph Representation Learning

arXiv

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arXiv 2023年

作者： Yan, Zuoyu Ma, Tengfei Gao, Liangcai Tang, Zhi Chen, Chao Wang, Yusu Wangxuan Institute of Computer Technology Peking University China Department of Biomedical Informatics Stony Brook University United States Halıcıoglu Data Science Institute University of California United States

Cycles are fundamental elements in graph-structured data and have demonstrated their effectiveness in enhancing graph learning models. To encode such information into a graph learning framework, prior works often extract a summary quantity, ranging from the number of cycles to the more sophisticated persistence diagram summaries. However, more detailed information, such as which edges are encoded in a cycle, has not yet been used in graph neural networks. In this paper, we make one step towards addressing this gap, and propose a structure encoding module, called CycleNet, that encodes cycle information via edge structure encoding in a permutation invariant manner. To efficiently encode the space of all cycles, we start with a cycle basis (i.e., a minimal set of cycles generating the cycle space) which we compute via the kernel of the 1-dimensional Hodge Laplacian of the input graph. To guarantee the encoding is invariant w.r.t. the choice of cycle basis, we encode the cycle information via the orthogonal projector of the cycle basis, which is inspired by BasisNet proposed by Lim et al. We also develop a more efficient variant which however requires that the input graph has a unique shortest cycle basis. To demonstrate the effectiveness of the proposed module, we provide some theoretical understandings of its expressive power. Moreover, we show via a range of experiments that networks enhanced by our CycleNet module perform better in various benchmarks compared to several existing SOTA models. Copyright © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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Time-dependent reduction of somatic variants in lichen striatus as evidence for the clearance of mutated skin cells

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Journal of the American Academy of Dermatology 2025年第6期92卷 1377-1379页

作者： Lee, Byunghyuk Lim, Dong Min Shin, Kihyuk Lee, Hyun Jung Lee, Jungsoo Kim, Hoon-Soo Kim, Byung-Soo Kim, Moon-Bum Kim, Kihun Kim, Won Kyu Kang, Junho Ko, Hyun-Chang Kim, Yun Hak Department of Dermatology College of Medicine Pusan National University Yangsan South Korea Interdisciplinary Program of Genomic Data Science Pusan National University Yangsan South Korea Department of Dermatology Pusan National University Yangsan Hospital Yangsan South Korea Research Institute for Convergence of Biomedical Science and Technology Pusan National University Yangsan Hospital Yangsan South Korea Department of Pathology School of Medicine Pusan National University Yangsan South Korea Department of Biomedical Informatics School of Medicine Pusan National University Yangsan South Korea Department of Anatomy School of Medicine Pusan National University Yangsan South Korea Natural Product Research Center Korea Institute of Science and Technology Gangneung South Korea Department of Research Keimyung University Dongsan Medical Center Daegu South Korea

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Predicting positive Clostridioides difficile test results using large-scale longitudinal data of demographics and medication history

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Heliyon 2025年第1期11卷 e41350页

作者： Pham, Anh El-Kareh, Robert Myers, Frank Ohno-Machado, Lucila Kuo, Tsung-Ting Division of Biomedical Informatics Department of Medicine University of California San Diego La Jolla CA United States UCSD Health System San Diego CA United States Department of Biomedical Informatics and Data Science Yale School of Medicine New Haven CT United States Department of Surgery Yale School of Medicine New Haven CT United States

Background: Clostridioides difficile infection is a major health threat. Healthcare institutions have strong medical and financial incentives to keep infections under control. Blanket testing at admission is in general not recommended, and current predictive models either used moderate sample sizes, over-inflated the number of covariates, or chose non-interpretable algorithms. We aim to develop models using patient data to predict positive Clostridioides difficile test results with discrimination performance, interpretable results, and a reasonable number of covariates that reflect health over a long-time span. Materials and methods: We processed records from 157,493 University of California San Diego Health patients seen between January 01, 2016–July 03, 2019 with at least 6 months of medication history, excluding pregnant women, patients under 18, and prisoners. Three models (Logistic Regression, Random Forest, and Ensemble) were constructed using hyper-parameters selected through 10-fold cross-validation. Model performance was measured by the Area Under the Receiver Operating Characteristic Curve (AUROC). The model coefficients’ odds ratios and p-values were calculated for the Logistic Regression model, as were Gini indices for Random Forest. Decision boundary analysis was conducted using pair-wise false positive and false negative cases each model would predict at a specific threshold. Results: Logistic Regression, Random Forest, and Ensemble models yielded test AUROCs of 0.839, 0.851, and 0.866, respectively. Significant covariates that may affect risk include age, immuno-compromised treatments, past antibiotic uses, and some medications for the gastrointestinal tract. Conclusions: The models achieve high discrimination performance (AUROC >0.83). There is a general consensus among different analysis approaches regarding predictors that impact patients' chances of having a positive test, which may influence Clostridioides difficile risk, including features clini

关键词： Clostridioides difficile infection Decision support systems Electronic health record Machine learning

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