New Insights into Machine Learning and Biomedicine: Updates and Directions, 2nd Edition

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 30 November 2025 | Viewed by 1039

Special Issue Editors


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Guest Editor
Faculty of Medical Bioengineering, University of Medicine and Pharmacy Grigore T. Popa, 700588 Iasi, Romania
Interests: medical engineering; biomedical instrumentation; biomedical device design; professional mentoring; medical device management
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Faculty of Medical Bioengineering, University of Medicine and Pharmacy Grigore T. Popa, 700588 Iasi, Romania
Interests: medical engineering; biomedical instrumentation; biomedical device design; professional mentoring; medical device management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Special Issue "New Insights into Machine Learning and Biomedicine: Updates and Directions, 2nd Edition" aims to offer a current and in-depth perspective on the state of the art in machine learning and its biomedical applications. It encompasses recent developments and emerging trends in computational intelligence and machine learning methods applied to a range of biomedical challenges, including disease diagnosis, treatment planning, drug discovery, and medical image analysis.

This Special Issue primarily seeks to define and contextualize the role of machine learning in biomedicine. It explores the latest machine learning methodologies relevant to the healthcare sector and evaluates their real-world impacts on both patients and medical professionals. Additionally, it aims to highlight current research directions and outline potential future applications of machine learning in the biomedical field.

This Special Issue seeks to bring together researchers, practitioners, and domain experts to share their knowledge, experiences, and perspectives on the most recent advancements in this dynamic and rapidly evolving field. Contributions are welcome on a wide range of topics, including, but not limited to, the following:

  • Machine learning algorithms and models for biomedical data analysis;
  • Deep learning and neural networks in biomedical applications;
  • Machine learning approaches in genomics and proteomics;
  • Medical image analysis and computer vision techniques;
  • Electronic health records and clinical decision support systems;
  • Predictive modeling and precision medicine;
  • Data integration and data mining in healthcare;
  • Ethical, legal, and societal implications of machine learning in biomedicine;
  • Integration of wearable device data into healthcare analytics;
  • Cloud-based AI platforms for large-scale biomedical data processing;
  • AI in public health surveillance and outbreak prediction.

The Special Issue also aims to explore future directions and key challenges in the field, emphasizing the transformative potential of machine learning in healthcare and its broader implications for society.

Dr. Cǎtǎlina Luca
Dr. Calin Corciova
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Bioengineering is an international peer-reviewed open access monthly journal published by MDPI.

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Keywords

  • machine learning
  • biomedicine
  • artificial intelligence
  • biomedical data analysis
  • deep learning
  • clinical decision support
  • predictive modeling
  • personalized medicine
  • bioinformatics
  • neural networks
  • explainable AI (XAI)
  • health informatics
  • omics data integration
  • data-driven in medicine
  • AI in healthcare
  • ethics in AI for medicine
  • future directions in biomedicine

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Published Papers (1 paper)

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Research

22 pages, 3356 KB  
Article
MS-LTCAF: A Multi-Scale Lead-Temporal Co-Attention Framework for ECG Arrhythmia Detection
by Na Feng, Chengwei Chen, Peng Du, Chengrong Gong, Jianming Pei and Dong Huang
Bioengineering 2025, 12(9), 1007; https://doi.org/10.3390/bioengineering12091007 - 22 Sep 2025
Viewed by 157
Abstract
Cardiovascular diseases are the leading cause of death worldwide, with arrhythmia being a prevalent and potentially fatal condition. The multi-lead electrocardiogram (ECG) is the primary tool for detecting arrhythmias. However, existing detection methods have shortcomings: they cannot dynamically integrate inter-lead correlations with multi-scale [...] Read more.
Cardiovascular diseases are the leading cause of death worldwide, with arrhythmia being a prevalent and potentially fatal condition. The multi-lead electrocardiogram (ECG) is the primary tool for detecting arrhythmias. However, existing detection methods have shortcomings: they cannot dynamically integrate inter-lead correlations with multi-scale temporal changes in cardiac electrical activity. They also lack mechanisms to simultaneously focus on key leads and time segments, and thus fail to address multi-lead redundancy or capture comprehensive spatial-temporal relationships. To solve these problems, we propose a Multi-Scale Lead-Temporal Co-Attention Framework (MS-LTCAF). Our framework incorporates two key components: a Lead-Temporal Co-Attention Residual (LTCAR) module that dynamically weights the importance of leads and time segments, and a multi-scale branch structure that integrates features of cardiac electrical activity across different time periods. Together, these components enable the framework to automatically extract and integrate features within a single lead, between different leads, and across multiple time scales from ECG signals. Experimental results demonstrate that MS-LTCAF outperforms existing methods. On the PTB-XL dataset, it achieves an AUC of 0.927, approximately 1% higher than the current optimal baseline model (DNN_zhu’s 0.918). On the LUDB dataset, it ranks first in terms of AUC (0.942), accuracy (0.920), and F1-score (0.745). Furthermore, the framework can focus on key leads and time segments through the co-attention mechanism, while the multi-scale branches help capture both the details of local waveforms (such as QRS complexes) and the overall rhythm patterns (such as RR intervals). Full article
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