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2nd International Workshop on Fairness of AI in Medical Imaging, FAIMI 2024, and 3rd International Workshop on Ethical and Philosophical Issues in Medical Imaging, EPIMI 2024, Held in Conjunction with the International Conference on Medical Image Computing and Computer Assisted Interventions, MICCAI 2024

作者： Stanley, Emma A. M. Souza, Raissa Winder, Anthony J. Wilms, Matthias Pike, G. Bruce Dagasso, Gabrielle Nielsen, Christopher MacEachern, Sarah J. Forkert, Nils D. Biomedical Engineering Graduate Program University of Calgary Calgary Canada Department of Radiology University of Calgary Calgary Canada Hotchkiss Brain Institute University of Calgary Calgary Canada Alberta Children’s Hospital Research Institute University of Calgary Calgary Canada Department of Pediatrics University of Calgary Calgary Canada

ISBN: (纸本)9783031727863

Clinical decision-making and radiology will inevitably be transformed by artificial intelligence (AI) in the coming years. The rapid adoption of AI in this domain, and in other everyday applications, has brought an increased awareness of the potential impacts and negative consequences that may occur throughout the sociotechnical systems that these technologies are implemented in. In this paper, we review and apply a previously published taxonomy of the sociotechnical harms of AI to investigate how these harms could manifest during the development and clinical implementation of AI-based medical image analysis. Through an illustrative case study example on computer-aided diagnosis using brain magnetic resonance imaging, we demonstrate how performing impact assessments of sociotechnical harms can assist in operationalizing the medical ethics principle of non-maleficence, thereby guiding the ethical development and implementation of AI technologies in healthcare. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Medical imaging

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Intelligent reflux and suction detection system for ventricular assist devices: in silico study

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Research on biomedical engineering 2025年第1期41卷 1-23页

作者： Santos, Bruno J. Cestari, Idágene A. Biomedical Engineering Graduate Program Polythecnic School of Engineering University of Sao Paulo Av. Prof. Luciano Gualberto Travessa 3 nº380 Sao Paulo05508-010 Brazil Department of Information Technology of Universidade São Judas Tadeu Rua Taquari nº546 Sao Paulo03166-000 Brazil Faculdade de Medicina Bioengineering Division - Heart Institute Hospital das Clinicas HCFMUSP Universidade de Sao Paulo LIM Bioengenharia Sao Paulo Sao Paulo Brazil

Introduction: Left ventricular assist devices (LVADs) serve both as a bridge to transplantation and as destination therapy for the treatment of congestive heart failure (CHF). However, the inability of the existing control strategies to automatically adapt LVAD flow according to hemodynamic changes can significantly impact patient survival and quality of life. Physiological control strategies for LVAD show promising results, with reflux and suction detection (RSD) increasing device safety. Methods: This study presents in silico results of an RSD system based on measurements of inlet and outlet pressures in continuous-flow LVAD. Two strategies were used to investigate control feasibility, safety, and adjustments to nonlinear variations and comprehensively assess the system’s state considering a structured algorithm (SA) and ensembles of AI models (eAIm): K-nearest neighbors (KNN), support vector machine (SVM), and artificial neural network (ANN). Results: The SA submodule achieved an accuracy of 99.66% in suction detection but showed limitations in reflux events, with 80.04% accuracy and an F1-Score of 70.4%. The KNN and SVM models demonstrated performance exceeding 96% for both events, exhibiting more excellent stability than the SA submodule. The ANN excelled with low variability and an RMSE of 0.07 in R1, though its suction accuracy (96.7%) was slightly lower than for reflux (99.48%). The KNN was the most effective model, achieving 99.66% accuracy in suction and 98.44% in reflux. The SVM also produced competitive results but with variability across evaluations. The eAIm model showed satisfactory precision (97.78% for suction and 97.14% for reflux), with variations depending on the scenario. The eAIm is recommended for optimization in precision-critical situations. Discussion: These strategies are designed to fulfill the proposal’s feasibility, flexibility, and safety requirements. They address the challenges of achieving consistent reproduction using an SA and the

关键词： Left ventricular assist devices

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Enhanced Performance and Data Privacy in Lung Nodule Classification via Federated Deep Learning Approach 24

Enhanced Performance and Data Privacy in Lung Nodule Classif...

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2024 7th International Conference on Healthcare Service Management, ICHSM 2024

作者： Nguyen, Duc-Khanh Li, Ai-Hsien Adam Lai, Yen-Jun Chiu, Yen-Ling Phan, Dinh-Van Chien, Ting-Ying Chan, Chien-Lung Department of Information Management Yuan Ze University Taoyuan Taiwan Department of Statistics and Informatics University of Economics The University of Danang Danang Viet Nam Division of Cardiology Far Eastern Memorial Hospital New Taipei Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Radiology Far Eastern Memorial Hospital New Taipei Taiwan Graduate Institute of Medicine and Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Medical Research Far Eastern Memorial Hospital New Taipei Taiwan Department of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan ZDT Group- YZU Joint Research and Development Center for Big Data Taoyuan Taiwan Yuan Ze University Taoyuan Taiwan

ISBN: (纸本)9798400710162

This study explores the feasibility of deep learning for classifying nodule neoplasms, analyzing their performance on two openly available datasets, LUNGx SPIE, and LIDC-IDRI. These datasets offer valuable diversity in image quantity, quality, and malignancy annotation methodologies, posing a real-world challenge for effective model development. To handle the disparities in these diverse datasets, we aim to develop a robust classification model trained using federated learning. This decentralized approach allows model training across datasets without sharing patient data, preserving privacy and overcoming data fragmentation. Our experiments demonstrate that federated learning enables the development of a robust and superior deep learning model compared to individual models trained on limited data. This combination of deep learning, and federated learning holds immense promise for nodule neoplasm classification and can significantly improve patient care and outcomes. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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Mammographic Breast Positioning Assessment via Deep Learning 1st

Mammographic Breast Positioning Assessment via Deep Learnin...

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1st Deep Breast Workshop on AI and Imaging for Diagnostic and Treatment Challenges in Breast Care, Deep-Breath 2024

作者： Tanyel, Toygar Denizoglu, Nurper Seker, Mustafa Ege Alis, Deniz Cerekci, Esma Karaarslan, Ercan Aribal, Erkin Oksuz, Ilkay Biomedical Engineering Graduate Program Istanbul Technical University Istanbul Turkey Department of Radiology Acibadem Healthcare Group Istanbul Turkey Department of Radiology University of Wisconsin-Madison MadisonWI United States Department of Radiology School of Medicine Acibadem University Istanbul Turkey Sisli Hamidiye Etfal Training and Research Hospital Istanbul Turkey Department of Computer Engineering Istanbul Technical University Istanbul Turkey

ISBN: (纸本)9783031777882

Breast cancer remains a leading cause of cancer-related deaths among women worldwide, with mammography screening as the most effective method for the early detection. Ensuring proper positioning in mammography is critical, as poor positioning can lead to diagnostic errors, increased patient stress, and higher costs due to recalls. Despite advancements in deep learning (DL) for breast cancer diagnostics, limited focus has been given to evaluating mammography positioning. This paper introduces a novel DL methodology to quantitatively assess mammogram positioning quality, specifically in mediolateral oblique (MLO) views using attention and coordinate convolution modules. Our method identifies key anatomical landmarks, such as the nipple and pectoralis muscle, and automatically draws a posterior nipple line (PNL), offering robust and inherently explainable alternative to well-known classification and regression-based approaches. We compare the performance of proposed methodology with various regression and classification-based models. The CoordAtt UNet model achieved the highest accuracy of 88.63% ± 2.84 and specificity of 90.25% ± 4.04, along with a noteworthy sensitivity of 86.04% ± 3.41. In landmark detection, the same model also recorded the lowest mean errors in key anatomical points and the smallest angular error of 2.42∘. Our results indicate that models incorporating attention mechanisms and CoordConv module increase the accuracy in classifying breast positioning quality and detecting anatomical landmarks. Furthermore, we make the labels and source codes available to the community to initiate an open research area for mammography, accessible at https://***/tanyelai/deep-breast-positioning. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Diseases

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Corrigendum to "Effect of cell imprinting on viability and drug susceptibility of breast cancer cells to doxorubicin" [Acta Biomaterialia, 2020, 113, 119-129] (Acta Biomaterialia (2020) 113 (119–129), (S1742706120303287), (10.1016/***.2020.06.007))

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Acta Biomaterialia 2025年 198卷 571-572页

作者： Shahriyari, Fatemeh Janmaleki, Mohsen Sharifi, Shahriar Hesar, Milad Eyvazi Hoshian, Sasha Taghiabadi, Reza Razaghian, Ahmad Ghadiri, Majid Peirovi, Afshin Mahmoudi, Morteza Nezhad, Amir Sanati Khademhosseini, Ali Division of Engineering in Medicine Department of Medicine Brigham and Women's Hospital Harvard Medical School CambridgeMA United States Division of Health Sciences & Technology Harvard-Massachusetts Institute of Technology Massachusetts Institute of Technology CambridgeMA United States Department of Materials Engineering Faculty of Engineering Imam Khomeini International University Qazvin Iran Biomedical Engineering Graduate Program University of Calgary Canada Precision Health Program and Department of Radiology Michigan State University East LansingMI48824 United States RWTH Aachen University Templergraben 55 Aachen52062 Germany Department of Chemistry and Materials Science Aalto University School of Chemical Engineering Espoo Finland Department of Mechanical Engineering Faculty of Engineering Imam Khomeini International University Qazvin Iran Nano-Biotechnology Department Pasteur Institute of Iran Tehran Iran Departments of Bioengineering Chemical and Biomolecular Engineering and Radiological Sciences Center for Minimally Invasive Therapeutics California NanoSystems Institute University of California – Los Angeles Los AngelesCA United States Terasaki Institute for Biomedical Innovation Los AngelesCA United States

The authors regret to report that the images for panels h (Plain PDMS) and I (MCF7-imprinted PDMS) in Fig. 3 were mistakenly taken from the same dataset, resulting in the omission of the correct image in panel h for the Plain PDMS day 7 image. We sincerely apologize for this error. For an accurate representation, please see the corrected Fig. 3 provided here along with its original figure caption. This does not affect the validity of the statistical analyses or the overall conclusions drawn from the study. © 2025

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Pilot Study of Distinct Graphs Models in Analysis of Brain Aging in Resting-State Functional Connectivity Networks

Pilot Study of Distinct Graphs Models in Analysis of Brain A...

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18th International Joint Conference on biomedical engineering Systems and Technologies, BIOSTEC 2025

作者： Carvalho, M.A.G. Frayne, R. School of Technology University of Campinas Brazil Radiology and Clinical Neurosciences Hotchkiss Brain Institute Univ. of Calgary Canada Seaman Family MR Research Centre Foothills Medical Centre Calgary Canada Biomedical Engineering Graduate Program University of Calgary Canada Calgary Image Processing and Analysis Centre Foothills Medical Centre Calgary Canada

Graphs have been used successfully to represent and analyze brain networks for many decades. Such structural and functional studies are important for revealing interactions between distinct areas of the brain that, for ex ample, are associated with the performance of a specific task or the onset of a cognitive disorder like dementia. In this pilot study, resting-state functional magnetic resonance imaging data were acquired in a sex-balanced sample of 10 young (20.1±2.1 years) and 10 old (65.6±0.4 years), presumed healthy, adults. We examined the effects of age on whole-brain resting-state functional connectivity (RSFC) networks. We examined two main graph modeling approaches to analyze RSFC networks. These approaches employ different strategies or graph models for thresholding over the complete network or examining changes in graph density. We com puted and compared one graph metric, the modularity, that was derived from the RSFC network graph models. Considering the need for a model that must preserve the network’s connectivity, strategies that use spanning trees as seeds to gradually increase the graph’s density seem more appropriate to represent brain networks. © 2025 by SCITEPRESS– Science and Technology Publications, Lda.

关键词： Magnetic resonance imaging

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First Results on Graph Similarity Search in Resting-State Functional Connectivity Networks Using Spectral and Graph Edit Distances

First Results on Graph Similarity Search in Resting-State Fu...

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18th International Joint Conference on biomedical engineering Systems and Technologies, BIOSTEC 2025

The application of graph theory in the modeling and analysis of brain networks has generated both new op portunities as well as new challenges in neuroscience. Resting state functional connectivity (RSFC) networks studied with graphs is an important field of investigation because of the potential benefits in understanding function in healthy individuals and identifying evidence of brain diseases and injury in patients. This work is unique because it applies information retrieval techniques to create ranked lists from RSFC graph theory derived networks. In our analysis, we used a sample of whole-brain resting-state functional magnetic reso nance imaging (rs-fMRI) data obtained from Young (n = 10, age: 20.1±2.1) and Old (n = 10, 65.6±0.4) sex-balanced groups drawn from a healthy, i.e., neurotypical, cohort. We estimated two well-known distance metrics (graph edit distance and graph spectral distance) and by using information-retrieval graph ranking methods achieved precision measures at the top-5 positions of ranked lists of up to 80%. © 2025 by SCITEPRESS– Science and Technology Publications, Lda.

关键词： Graph neural networks

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Nanoplasmonic Biosensors: A Comprehensive Overview and Future Prospects

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International Journal of Nanomedicine 2025年 20卷 5817-5836页

作者： Mutalik, Chinmaya Sharma, Shashwat Yougbaré, Sibidou Chen, Chih-Yu Kuo, Tsung-Rong Graduate Institute of Nanomedicine and Medical Engineering College of Biomedical Engineering Taipei Medical University Taipei 11031 Taiwan Center for Airborne Infection & Transmission Science Tulane University School of Medicine New Orleans 70112 LA United States International Ph.D. Program in Medicine College of Medicine Taipei Medical University Taipei 11031 Taiwan Institut de Recherche en Sciences de La Santé/Direction Régionale du Centre Ouest (IRSS/DRCO) BP 21811 Nanoro Burkina Faso Department of Orthopedics Shuang Ho Hospital Taipei Medical University New Taipei City 23561 Taiwan International Ph.D. Program in Biomedical Engineering College of Biomedical Engineering Taipei Medical University Taipei 11031 Taiwan School of Biomedical Engineering Taipei Medical University Taipei 11031 Taiwan

Recent nanotechnological advancements have resulted in a paradigm shift in biosensing applications through the advent of nanoplasmonic biosensors. These devices integrate nanomaterials with phenomena like surface plasmon resonance (SPR) and localized SPR (LSPR) to address the critical diagnostic and analytical needs across medicine, food safety, and drug discovery. Leveraging metals like gold and silver, these sensors exhibit enhanced optical and electronic properties, enabling the detection of biomolecules at ultralow concentrations. However, despite their transformative potential, challenges concerning stability, reproducibility, cost-efficiency, and scalability impede widespread implementation. This review offers a rigorous analysis of nanoplasmonic biosensors, emphasizing their underlying operational mechanisms and diverse applications. It also delves into design paradigms, fabrication protocols, and optimization strategies while concurrently examining prevailing challenges and prospective advancements. Furthermore, it highlights emerging trends, such as hybrid plasmonic nanostructures, conferring advantages in miniaturization, automation, and high-throughput analysis, thereby establishing a robust foundation for future innovation in the field. © 2025 Mutalik et al.

关键词： device nanomaterial nanoplasmonic optical biosensor SPR

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3D-Printable Photothermal and Temperature-Controlled Polycaprolactone Scaffolds Incorporating Gold Plasmonic Blackbodies for Bone Tissue engineering

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ACS Applied Materials and Interfaces 2025年第20期17卷 29455-29468页

作者： Chen, Chieh-Ying Hope Gadia Moreno, Ruaina Lily Wang, Po-Yao Nguyen, Thanh Sang Wu, Jia-Lin Chen, Kuan-Hao Chen, Chih-Hwa Lin, Chia-Ying Wong, Pei-Chun Graduate Institute of Biomedical Optomechatronics College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan School of Biomedical Engineering College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan Graduate Institute of Biomedical Materials and Tissue Engineering College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan Department of Orthopedics Taipei Medical University Hospital Taipei11031 Taiwan International Ph.D. Program in Medicine College of Medicine Taipei Medical University Taipei11031 Taiwan Department of Trauma Hue Central Hospital Hue530000 Viet Nam Department of Orthopedics School of Medicine College of Medicine Taipei Medical University Taipei11031 Taiwan Orthopedics Research Center Taipei Medical University Hospital Taipei11031 Taiwan Centers for Regional Anesthesia and Pain Medicine Wan Fang Hospital Taipei Medical University Taipei11096 Taiwan Department of Orthopedics Shuang Ho Hospital Taipei Medical University New Taipei23561 Taiwan Convergent Bioscience and Technology Institute Department of Biomedical Engineering and Informatics Indiana University IndianapolisIN46202 United States

Three-dimensional (3D) printing technology has revolutionized the design and fabrication of bone scaffolds, offering precise and customizable solutions for bone tissue engineering. In this study, we developed polycaprolactone (PCL) scaffolds that incorporated gold plasmonic blackbodies (AuPBs) to harness photothermal properties for temperature-controlled bone regeneration. The AuPB-PCL scaffolds demonstrated enhanced mechanical strength, a tunable thermal response under near-infrared (NIR) laser irradiation, and improved osteogenic potential. Photothermal stimulation effectively modulated cellular responses, promoting osteoblast proliferation, alkaline phosphatase (ALP) activity, and mineralization. Notably, mild hyperthermia (39-41 °C) induced by laser irradiation optimized osteogenesis, while excessive temperatures (≥42.5 °C) impaired cellular function due to mitochondrial stress and oxidative damage. These findings highlight the potential of AuPB-PCL scaffolds for controlled photothermal bone regeneration, offering a promising strategy for precise, completely noninvasive stimulation of bone repair. © 2025 The Authors. Published by American Chemical Society.

关键词： Fiber bonding

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ATP_mCNN: Predicting ATP binding sites through pretrained language models and multi-window neural networks

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Computers in Biology and Medicine 2025年 185卷 109541-109541页

作者： Le, Van-The Malik, Muhammad-Shahid Lin, Yi-Jing Liu, Yu-Chen Chang, Yan-Yun Ou, Yu-Yen Department of Computer Science and Engineering Yuan Ze University Chung-Li32003 Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Chung-Li32003 Taiwan Department of Computer Sciences Karakoram International University Gilgit-Baltistan 15100 Pakistan

Adenosine triphosphate plays a vital role in providing energy and enabling key cellular processes through interactions with binding proteins. The increasing amount of protein sequence data necessitates computational methods for identifying binding sites. However, experimental identification of adenosine triphosphate-binding residues remains challenging. To address the challenge, we developed a multi-window convolutional neural network architecture taking pre-trained protein language model embeddings as input features. In particular, multiple parallel convolutional layers scan for motifs localized to different window sizes. Max pooling extracts salient features concatenated across windows into a final multi-scale representation for residue-level classification. On benchmark datasets, our model achieves an area under the ROC curve of 0.95, significantly improving on prior sequence-based models and outperforming convolutional neural network baselines. This demonstrates the utility of pre-trained language models and multi-window convolutional neural networks for advanced sequence-based prediction of adenosine triphosphate-binding residues. Our approach provides a promising new direction for elucidating binding mechanisms and interactions from primary structure. © 2024 Elsevier Ltd

关键词： Convolutional neural networks

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