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Bioengineering
Special Issues
Biomedical Applications of Multimodal Imaging Combined with Artificial Intelligence
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Biomedical Applications of Multimodal Imaging Combined with Artificial Intelligence

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

Deadline for manuscript submissions: 31 October 2025 | Viewed by 2802

Special Issue Editor

Prof. Dr. Muhammad Umair Ali

E-Mail Website
Guest Editor
Department of Artificial Intelligence and Robotics, Sejong University, Seoul 05006, Republic of Korea
Interests: system modeling; states estimation; optimization; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue explores integrating multimodal imaging techniques with artificial intelligence (AI) to advance biomedical applications. It highlights the potential of combining various multimodal imaging modalities, such as MRI, CT, PET, and ultrasound, and provides comprehensive insights into biological tissues' anatomical and functional aspects. Combined with AI algorithms, these imaging techniques can significantly enhance disease diagnosis, treatment planning, and patient monitoring accuracy and efficiency.

This Special Issue explores integrating AI techniques with image modality systems to revolutionize biomedical applications. It aims to present work from researchers and practitioners from multidisciplinary backgrounds and discuss the latest advancements, challenges, and future prospects in this rapidly growing field.

Topics of interest within this Special Issue include, but are not limited to, the development and evaluation of novel AI algorithms in biomedical imaging, the application of machine learning techniques to enhance detection and diagnosis accuracy, the utilization of deep learning architectures for integrating multimodal imaging, the integration of AI technologies into medical decision making, and the impact of AI on diagnosis and treatment planning.

This Special Issue features a collection of cutting-edge research articles, case studies, and reviews that demonstrate the innovative applications of AI-driven multimodal imaging in various biomedical domains.

Prof. Dr. Muhammad Umair Ali
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • multimodal imaging
  • magnetic resonance imaging (MRI)
  • computerized tomography (CT)
  • positron emission tomography (PET)
  • functional near-infrared spectroscopy (fNIRS)
  • AI
  • EEG

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Published Papers (2 papers)

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Research

19 pages, 881 KiB  
Article
Cross-Subject Emotion Recognition with CT-ELCAN: Leveraging Cross-Modal Transformer and Enhanced Learning-Classify Adversarial Network
by Ping Li, Ao Li, Xinhui Li and Zhao Lv
Bioengineering 2025, 12(5), 528; https://doi.org/10.3390/bioengineering12050528 - 15 May 2025
Viewed by 328
Abstract
Multimodal physiological emotion recognition is challenged by modality heterogeneity and inter-subject variability, which hinder model generalization and robustness. To address these issues, this paper proposes a new framework, Cross-modal Transformer with Enhanced Learning-Classifying Adversarial Network (CT-ELCAN). The core idea of CT-ELCAN is to [...] Read more.
Multimodal physiological emotion recognition is challenged by modality heterogeneity and inter-subject variability, which hinder model generalization and robustness. To address these issues, this paper proposes a new framework, Cross-modal Transformer with Enhanced Learning-Classifying Adversarial Network (CT-ELCAN). The core idea of CT-ELCAN is to shift the focus from conventional signal fusion to the alignment of modality- and subject-invariant emotional representations. By combining a cross-modal Transformer with ELCAN, a feature alignment module using adversarial training, CT-ELCAN learns modality- and subject-invariant emotional representations. Experimental results on the public datasets DEAP and WESAD demonstrate that CT-ELCAN achieves accuracy improvements of approximately 7% and 5%, respectively, compared to state-of-the-art models, while also exhibiting enhanced robustness. Full article
(This article belongs to the Special Issue Biomedical Applications of Multimodal Imaging Combined with Artificial Intelligence)
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16 pages, 3950 KiB  
Article
MRI-Driven Alzheimer’s Disease Diagnosis Using Deep Network Fusion and Optimal Selection of Feature
by Muhammad Umair Ali, Shaik Javeed Hussain, Majdi Khalid, Majed Farrash, Hassan Fareed M. Lahza and Amad Zafar
Bioengineering 2024, 11(11), 1076; https://doi.org/10.3390/bioengineering11111076 - 28 Oct 2024
Cited by 2 | Viewed by 2135
Abstract
Alzheimer’s disease (AD) is a degenerative neurological condition characterized by cognitive decline, memory loss, and reduced everyday function, which eventually causes dementia. Symptoms develop years after the disease begins, making early detection difficult. While AD remains incurable, timely detection and prompt treatment can [...] Read more.
Alzheimer’s disease (AD) is a degenerative neurological condition characterized by cognitive decline, memory loss, and reduced everyday function, which eventually causes dementia. Symptoms develop years after the disease begins, making early detection difficult. While AD remains incurable, timely detection and prompt treatment can substantially slow its progression. This study presented a framework for automated AD detection using brain MRIs. Firstly, the deep network information (i.e., features) were extracted using various deep-learning networks. The information extracted from the best deep networks (EfficientNet-b0 and MobileNet-v2) were merged using the canonical correlation approach (CCA). The CCA-based fused features resulted in an enhanced classification performance of 94.7% with a large feature vector size (i.e., 2532). To remove the redundant features from the CCA-based fused feature vector, the binary-enhanced WOA was utilized for optimal feature selection, which yielded an average accuracy of 98.12 ± 0.52 (mean ± standard deviation) with only 953 features. The results were compared with other optimal feature selection techniques, showing that the binary-enhanced WOA results are statistically significant (p < 0.01). The ablation study was also performed to show the significance of each step of the proposed methodology. Furthermore, the comparison shows the superiority and high classification performance of the proposed automated AD detection approach, suggesting that the hybrid approach may help doctors with dementia detection and staging. Full article
(This article belongs to the Special Issue Biomedical Applications of Multimodal Imaging Combined with Artificial Intelligence)
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