Artificial Intelligence Revolution in Biomedical Image and Signal Processing: Innovations and Applications

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

Deadline for manuscript submissions: 30 June 2025 | Viewed by 2492

Special Issue Editors


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Guest Editor
Department of Electronic Engineering, Chung Yuan Christian University, Taoyuan City 32023, Taiwan
Interests: medical image processing; artificial intelligence; image processing; data compression; biomedical signal processing and systems; VLSI chip design; wireless body sensor networks

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Guest Editor
Department of Microelectronics, Fuzhou University, Fuzhou 350116, China
Interests: low-power biological signal acquisition; detection ICs design; automatic identification; classification of heart disease analysis; ECG images features; brain–computer interface; EEG signal analysis; cardiac medical information; brain science heterogeneous data processing
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Electronic Engineering, Feng Chia University, Taichung 40724, Taiwan
Interests: medical image processing; deep leaning; digital chip design; system-on-chip (SoC); digital circuits

Special Issue Information

Dear Colleagues,

In recent years, the rapid advancement of artificial intelligence (AI) has sparked a technological revolution. AI has made significant strides in the field of biomedical image and signal processing. Breakthroughs and advancements in precision medicine technologies are enhancing clinical diagnostics and offering treatment recommendations. With the complete digitization of biomedical images and signals and ongoing innovations in AI, the integration of AI with biomedical images and signals is aimed at improving diagnostic accuracy and reducing the risk of human error.

Therefore, this Special Issue, entitled “Artificial Intelligence Revolution in Biomedical Image and Signal Processing: Innovations and Applications”, aims to spotlight cutting-edge research and in-depth reviews in fields like computational biomedical image processing, biomedical signal processing, multimodal medical image information fusion, quality assessment, and biomedical image enhancement. By focusing on these critical areas, the Special Issue intends to serve as a key publication platform for addressing pivotal challenges and breakthroughs related to AI innovations and applications in biomedical image and signal processing. We invite contributions that explore novel techniques, practical implementations, and theoretical advancements, ensuring a comprehensive discourse on the transformative impact of AI in this domain.

Topics of interest for submissions to this Special Issue include, but are not limited to, the following:

  • AI-based medical image processing;
  • Biomedical signal and image processing powered by AI;
  • Intelligent systems for medical imaging;
  • Innovations and practices of deep learning technologies in medical imaging;
  • AI techniques for biomedical image processing;
  • AI techniques for biomedical signal and image processing;
  • Applications of machine learning techniques and deep learning in medical imaging;
  • Neural network applications in biomedical imaging;
  • AI methods applied to diagnostic imaging;
  • AI-assisted diagnosis and image-guided surgery in clinical applications;
  • Explorations of the revolutionary role of AI in early disease detection, particularly in cancer and other critical-disease diagnoses at early stages;
  • Detailed investigations of multimodal medical image fusion and intelligent diagnostic systems.

Dr. Shih-Lun Chen
Prof. Dr. Liang-Hung Wang
Dr. Tsung-Yi Chen
Guest Editors

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Keywords

  • artificial intelligence techniques for biomedical signal and image processing
  • medical image processing
  • deep learning
  • AI-based diagnostic imaging
  • multimodal image fusion
  • early disease detection
  • image-guided surgery

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Published Papers (2 papers)

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Research

26 pages, 6715 KiB  
Article
Feature Feedback-Based Pseudo-Label Learning for Multi-Standards in Clinical Acne Grading
by Yung-Yao Chen, Hung-Tse Chan, Hsiao-Chi Wang, Chii-Shyan Wang, Hsuan-Hsiang Chen, Po-Hua Chen, Yi-Ju Chen, Shao-Hsuan Hsu and Chih-Hsien Hsia
Bioengineering 2025, 12(4), 342; https://doi.org/10.3390/bioengineering12040342 - 26 Mar 2025
Viewed by 922
Abstract
Accurate acne grading is critical in optimizing therapeutic decisions yet remains challenging due to lesion ambiguity and subjective clinical assessments. This study proposes the Feature Feedback-Based Pseudo-Label Learning (FF-PLL) framework to address these limitations through three innovations: (1) an acne feature feedback (AFF) [...] Read more.
Accurate acne grading is critical in optimizing therapeutic decisions yet remains challenging due to lesion ambiguity and subjective clinical assessments. This study proposes the Feature Feedback-Based Pseudo-Label Learning (FF-PLL) framework to address these limitations through three innovations: (1) an acne feature feedback (AFF) architecture with iterative pseudo-label refinement to improve the training robustness, enhance the pseudo-label quality, and increase the feature diversity; (2) all-facial skin segmentation (AFSS) to reduce background noise, enabling precise lesion feature extraction; and (3) the AcneAugment (AA) strategy to foster model generalization by introducing diverse acne lesion representations. Experiments on the ACNE04 and ACNE-ECKH benchmark datasets demonstrate the superiority of the proposed framework, achieving accuracy of 87.33% on ACNE04 and 67.50% on ACNE-ECKH. Additionally, the model attains sensitivity of 87.31%, specificity of 90.14%, and a Youden index (YI) of 77.45% on ACNE04. These advancements establish FF-PLL as a clinically viable solution for standardized acne assessment, bridging critical gaps between computational dermatology and practical healthcare needs. Full article
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22 pages, 8768 KiB  
Article
Deep Learning-Assisted Diagnostic System: Apices and Odontogenic Sinus Floor Level Analysis in Dental Panoramic Radiographs
by Pei-Yi Wu, Yuan-Jin Lin, Yu-Jen Chang, Sung-Tsun Wei, Chiung-An Chen, Kuo-Chen Li, Wei-Chen Tu and Patricia Angela R. Abu
Bioengineering 2025, 12(2), 134; https://doi.org/10.3390/bioengineering12020134 - 30 Jan 2025
Viewed by 956
Abstract
Odontogenic sinusitis is a type of sinusitis caused by apical lesions of teeth near the maxillary sinus floor. Its clinical symptoms are highly like other types of sinusitis, often leading to misdiagnosis as general sinusitis by dentists in the early stages. This misdiagnosis [...] Read more.
Odontogenic sinusitis is a type of sinusitis caused by apical lesions of teeth near the maxillary sinus floor. Its clinical symptoms are highly like other types of sinusitis, often leading to misdiagnosis as general sinusitis by dentists in the early stages. This misdiagnosis delays treatment and may be accompanied by toothache. Therefore, using artificial intelligence to assist dentists in accurately diagnosing odontogenic sinusitis is crucial. This study introduces an innovative odontogenic sinusitis image processing technique, which is fused with common contrast limited adaptive histogram equalization, Min-Max normalization, and the RGB mapping method. Moreover, this study combined various deep learning models to enhance diagnostic accuracy. The YOLO 11n model was used to detect odontogenic sinusitis single tooth position in dental panoramic radiographs and achieved an accuracy of 98.2%. The YOLOv8n-cls model diagnosed odontogenic sinusitis with a final classification accuracy of 96.1%, achieving a 16.9% improvement over non-enhanced methods and outperforming recent studies by at least 4%. Additionally, in clinical applications, the classification accuracy for non-odontogenic sinusitis was 95.8%, while for odontogenic sinusitis it was 97.6%. The detection method developed in this study effectively reduces the radiation dose patients receive during CT imaging and serves as an auxiliary system, providing dentists with reliable support for the precise diagnosis of odontogenic sinusitis. Full article
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