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30th International Conference on Intelligent User Interfaces Companion, IUI 2025

作者： Soufargi, Selim Niewiadomski, Radoslaw Fong, Hunter Mancini, Maurizio Department of Computer Science Sapienza University of Rome Rome Italy Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Genoa Italy

ISBN: (纸本)9798400714092

In this paper, we present the first implementation of a social robot acting as a companion for individuals eating alone. Our system can engage in multimodal interactions with the user during meals. It conducts conversations on various topics, complemented by gestures and other nonverbal behaviors. The system comprises a NAO robot, human activity recognition modules, a speech recognition module, and a dialog management system integrated with an LLM model. In the paper, we present a technical description of the system and the interaction flow, as well as discuss the possible applications and planned validation. © 2025 Copyright held by the owner/author(s).

关键词： Social robots

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Rapid Amplification of Single-Stranded DNA by On-Bead Plasmonic PCR in an Automated Microfluidic System 38

Rapid Amplification of Single-Stranded DNA by On-Bead Plasmo...

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38th IEEE International Conference on Micro Electro Mechanical Systems, MEMS 2025

作者： Hu, Anni Bu, Yang Liu, Yuze Lee, Yung Ching Yobas, Levent The Hong Kong University of Science and Technology Division of Bioengineering Hong Kong The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong

ISBN: (纸本)9798331508890

This paper presents a novel automated microfluidic system for rapid plasmonic on-beads polymerase chain reaction (PCR). The highly integrated system automates essential PCR workflows, including sample loading, thermal cycling, purification washes, and elution utilizing pneumatic valves. Additionally, gold nanorods are introduced into the PCR system to leverage their efficient photothermal conversion properties to accelerate the PCR reaction. The platform completes 20 PCR cycles within 12 minutes to amplify single-stranded DNA (ssDNA), demonstrating potential for various rapid ssDNA PCR applications, such as aptamer selection and generation. © 2025 IEEE.

关键词： Microfluidics

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Scanning the Issue

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IEEE Transactions on Intelligent Transportation Systems 2025年第1期26卷 3-21页

作者： Simona Sacone Department of Computer Science Bioengineering Robotics and Systems Engineering University of Genova Genova Italy

“Scanning the Issue.“

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Scanning the Issue - May 2025

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IEEE Transactions on Intelligent Transportation Systems 2025年第5期26卷 5627-5648页

作者： Simona Sacone Department of Computer Science Bioengineering Robotics and Systems Engineering University of Genova Genova Italy

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Faster Amylin Aggregation on Fibrillar Collagen I Hastens Diabetic Progression through β-Cell Death and Loss of Function

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Journal of the American Chemical Society 2025年第19期147卷 15985-16006页

作者： Hazari, Md Asrafuddoza Kannan, Gautam Dasgupta, Subrata Pavan, Musale Krushna Jha, Akash Kumar Sultana, Farhin Pujahari, Soumya Ranjan Singh, Simran Dutta, Sarbajeet Pydi, Sai Prasad Dutta, Sankhadeep Zafar, Hamim Bhaumik, Prasenjit Kumar, Ashutosh Sen, Shamik Department of Biosciences & Bioengineering IIT Bombay Mumbai400076 India Department of Biological Sciences and Bioengineering IIT Kanpur Kanpur208016 India Department of Oncogene Regulation CNCI Kolkata700026 India Department of Computer Science and Engineering IIT Kanpur Kanpur208016 India Mehta Family Centre for Engineering in Medicine IIT Kanpur Kanpur208016 India

Amyloid deposition of the neuroendocrine peptide amylin in islet tissues is a hallmark of type 2 diabetes (T2DM), leading to β-cell toxicity through nutrient deprivation, membrane rupture, and apoptosis. Though accumulation of toxic amylin aggregates in islet matrices is well documented, the role of the islet extracellular matrix in mediating amylin aggregation and its pathological consequences remains elusive. Here, we address this question by probing amylin interaction with collagen I (Col)─whose expression in the islet tissue increases during diabetes progression. By combining multiple biophysical techniques, we show that hydrophobic, hydrophilic, and cation-π interactions regulate amylin binding to Col, with fibrillar Col driving faster amylin aggregation. Amylin-entangled Col matrices containing high amounts of amylin induce death and loss of function in INS1E β-cells. Together, our results illustrate how amylin incorporation in islet matrices through amylin-Col interactions drives T2DM progression by impacting β-cell viability and insulin secretion. © 2025 American Chemical Society.

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Comprehensive multimodal approach for Parkinson’s disease classification using artificial intelligence: insights and model explainability

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Soft Computing 2025年第3期29卷 1845-1877页

作者： Balaha, Hossam Magdy Hassan, Asmaa El-Sayed Ahmed, Rawan Ayman Balaha, Magdy Hassan Bioengineering Department J.B. Speed School of Engineering University of Louisville LouisvilleKY United States Computer Science and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt Mathematics and Engineering Physics Department Faculty of Engineering Mansoura University Mansoura Egypt Department of Obstetrics and Gynecology Faculty of Medicine Tanta University Tanta Egypt

Parkinson’s disease (PD) is a debilitating neurodegenerative disorder affecting millions worldwide. Early detection is vital for effective management, yet remains challenging. In this study, we investigated four distinct datasets for PD detection. Through comprehensive experimentation employing ensemble methods and feature selection, we achieved high classification accuracies across the datasets. For the Oxford Parkinson’s Disease Detection Dataset, an accuracy of 95.67%, precision of 97.59%, recall of 84.5%, specificity of 99.32%, and F1-score of 90.57% were achieved. For the Alzheimer Parkinson Diseases 3 Class Dataset, the "Stacking" approach surpasses individual models, reaching an accuracy of 99.85%, precision of 99.81%, recall of 99.81%, specificity of 99.86%, and F1 of 99.81%. For the NewHandPD dataset, Regarding the Spiral category, The "Base-P32-384" model surpasses others with an accuracy of 97.35%, precision of 96.50%, recall of 98.57%, and F1-score of 97.53%. The collective "Stacking" approach proves highly effective regarding the Circle category, achieving 100% across all performance metrics. Regarding the Meander category, the "Base-P16-224" model achieves an accuracy of 97.35%, precision of 99.26%, recall of 95.71%, specificity of 99.19%, and F1 of 97.45%. The Mobile Device Voice Recordings at King’s College London (MDVR-KCL) dataset contains two datasets. Regarding the "SpontaneousDialogue" dataset, accuracy, BAC, precision, recall, specificity, and F1-score were computed, resulting in values of 94.03%, 92.83%, 90.78%, 100.0%, and 85.67%, respectively. Regarding the "ReadText" dataset, accuracy, BAC, precision, recall, specificity, and F1-score were computed, resulting in values of 91.89%, 90.62%, 87.5%, 100.0%, and 81.25%, respectively. Our findings highlight the efficacy of leveraging diverse data sources and advanced machine learning techniques to enhance PD detection accuracy. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germa

关键词： Neurodegenerative diseases

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Normative Modeling with Focal Loss and Adversarial Autoencoders for Alzheimer’s Disease Diagnosis and Biomarker Identification 3rd

Normative Modeling with Focal Loss and Adversarial Autoenc...

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3rd International Workshop on Applications of Medical Artificial Intelligence, AMAI 2024 held in conjunction with the 27th International Conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2024

作者： Zhao, Songlin Zhou, Rong Zhang, Yu Chen, Yong He, Lifang Department of Computer Science and Engineering Lehigh University BethlehemPA United States Department of Bioengineering Lehigh University BethlehemPA United States Department of Biostatistics Epidemiology and Informatics University of Pennsylvania PhiladelphiaPA United States

ISBN: (纸本)9783031820069

In this paper, we introduce a novel normative modeling approach that incorporates focal loss and adversarial autoencoders (FAAE) for Alzheimer’s Disease (AD) diagnosis and biomarker identification. Our method is an end-to-end approach that embeds an adversarial focal loss discriminator within the autoencoder structure, specifically designed to effectively target and capture more complex and challenging cases. We first use the enhanced autoencoder to create a normative model based on data from healthy control (HC) individuals. We then apply this model to estimate total and regional neuroanatomical deviation in AD patients. Through extensive experiments on the OASIS-3 and ADNI datasets, our approach significantly outperforms previous state-of-the-art methods. This advancement not only streamlines the detection process but also provides a greater insight into the biomarker potential for AD. Our code can be found at https://***/soz223/FAAE. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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AI-Driven Race Prediction from Mammographic Images: Anatomical Insights for AI Model’s Bias Mitigation

AI-Driven Race Prediction from Mammographic Images: Anatomic...

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Medical Imaging 2025: computer-Aided Diagnosis

作者： Mustafaev, Tamerlan Hossain, Md Belayat Nishikawa, Robert M. Lee, Juhun Department of Radiology University of Pittsburgh PittsburghPA United States Department of Bioengineering University of Pittsburgh PittsburghPA United States Department of Computer Science Southern Illinois University CarbondaleIL United States

ISBN: (纸本)9781510685925

Bias in datasets and AI models have emerged as a significant challenge in the medical AI community, particularly due to the underrepresentation of certain racial groups and the overrepresentation of others, leading to performance degradation in real clinical practice. This underscores the critical need for further and deeper analysis of racial differences to address these issues effectively. Despite its importance, research on imaging race differences is scarce. This study explores race-based anatomical differences in mammographic images among Asian and White women populations. We extracted mammographic images of 1043 Asian and 986 White individuals from the EMBED dataset, ensuring that the development dataset was matched for age and BIRADS breast density. For independent testing, we extracted mammograms from 157 Asian and 673 White women from the University of Pittsburgh Medical Center (UPMC). We found that mammograms from these groups have notable anatomical differences. Specifically, we developed a Vision Transformer (ViT) model to predict race (Asian and White) in mammographic images, achieving the test AUC of 0.82 from the development and 0.74 from external validation for original images. We also used each cohort's binary breast density (representing fibroglandular tissue distribution) and binary breast area (representing breast size and shape) masks to analyze spatial breast density distribution, breast volume, and shape differences between races. Our classifier predicted race based on binary dense masks, yielding a result of 0.70/0.71 for AUC (development/external testing datasets);for binary breast area masks, the classifier achieved 0.60/0.70 (development/external testing datasets). We found that the main anatomical difference between Asian and White races is the breast size followed by breast shape. This result can be used to address racial bias between Asian and White races to ensure equitable performance of AI models in clinical settings. © 2025 SPIE.

关键词： Mammography

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Colosseum Chart: A novel chart to describe the Temporal Relationship of categorical features

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Procedia computer science 2025年 258卷 1295-1301页

作者： Deep Patel Pranav Goswami Mitali Mukerji Suman Kundu Department of Computer Science and Engineering IIT Jodhpur Rajasthan 342037 India Department of Bioscience & Bioengineering IIT Jodhpur Rajasthan 342037 India

Visualisation is an important first step for exploratory data analysis and imperative for any data-driven scientific endeavor. This paper proposes a new visualization, namely the Colosseum chart, which is capable of representing relationships between data, data attributes, and time. The proposed diagram is a circular diagram containing multiple rings to capture dimension of the data. Additionally, a color-coded heat map represents the data values. This diagram illustrates the temporal progression or variations in the data, enabling comparisons among categorical data across related attributes. The case studies are presented where the diagram provided a better understanding of the data compared to the Sankey diagram, a natural choice for showing the flow of information. The developed tool for generating Colosseum charts is hosted on https://***/Colosseum-Chart/ . The code repository is available at https://***/Pranavchiku/Colosseum-Chart/

关键词： Multidimensional Visualisation Network Visualisation Circular Diagrams Heatmap Biological Data Visualisation

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Developing Breast Dense Tissue Segmentation Algorithm in Digital Breast Tomosynthesis

Developing Breast Dense Tissue Segmentation Algorithm in Dig...

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Medical Imaging 2025: computer-Aided Diagnosis

作者： Hossain, Md Belayat Mustafaev, Tamerlan Nishikawa, Robert M. Lee, Juhun Department of Computer Science Southern Illinois University CarbondaleIL United States Department of Radiology University of Pittsburgh PittsburghPA United States Department of Bioengineering University of Pittsburgh PittsburghPA United States

ISBN: (纸本)9781510685925

Digital Breast Tomosynthesis (DBT) is an imaging modality that has proven to more effective for breast cancer screening, which is important for reducing mortality. However, existing studies on segmenting dense tissue in DBT remain limited, largely due to the challenges posed by the complexity of multi-slice variations, blurring, and out-of-plane artifacts. This study introduced a fully automated dense tissue segmentation algorithm in DBT image using a fully convolutional network. We employed 20 DBT scans from 20 normal patients (BIRADS 1) from the Breast-Cancer-Screening-DBT dataset. For establishing ground truth, one radiologist segmented breast dense tissue, and breast area mask in every slice of each DBT volume. We preprocessed each DBT volume slice-by-slice. Finally, we constructed 3-channel images (ground truth masks) by assigning breast area, fatty and dense area into R, G, B channels. Using the DBT images and ground truth, we fine-tuned the SegNet pretrained for segmenting breast density from 2D mammograms to segment both the breast and the fibroglandular areas in digital breast tomosynthesis (DBT) images. Using a test set, we achieved a dice score of 0.84±0.08, where a recent study reported a dice score of 0.76±0.16 for dense tissue segmentation in DBT. Our model performed better than the other models in the DBT. © 2025 SPIE.

关键词： Image segmentation

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