Submit to Bioengineering Review for Bioengineering Propose a Special Issue

Journal Menu

Journal Browser

► Journal Browser

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

Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

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

Deadline for manuscript submissions: closed (31 May 2026) | Viewed by 18932

Share This Special Issue

Editors

Dr. Shih-Lun Chen

E-Mail Website
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

Prof. Dr. Liang-Hung Wang

E-Mail Website
Guest Editor

Department of Microelectronics, Fuzhou University, 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

Dr. Tsung-Yi Chen

E-Mail Website
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

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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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

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

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (8 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

Jump to: Other

16 pages, 4741 KB

Open AccessArticle

Robust Non-Invasive Cardiac Index Prediction via Feature Integration and Data-Augmented Neural Networks

by Chih-Hao Chang, Mei-Ling Chan, Yu-Hung Fang, Po-Lin Huang, Tsung-Yi Chen, Tsun-Kuang Chi, I Elizabeth Cha, Tzong-Rong Ger, Kuo-Chen Li, Shih-Lun Chen, Liang-Hung Wang, Jia-Ching Wang and Patricia Angela R. Abu

Bioengineering 2026, 13(4), 477; https://doi.org/10.3390/bioengineering13040477 - 18 Apr 2026

Viewed by 792

Abstract

Concurrent with the rising consumption of ultra-processed, high-calorie diets and the decline in physical activity, obesity and related cardiovascular conditions among young adults have continued to increase, becoming an important global public health concern. This study integrates non-invasive Internet of Things (IoT) sensing devices, including the TERUMO ES-P2000 blood pressure monitor (Terumo Corp., Tokyo, Japan) and the PhysioFlow PF07 Enduro cardiac hemodynamic analyzer (Manatec Biomedical, Poissy, France), with an artificial neural network (ANN) for cardiac index (CI) prediction. Through appropriate data preprocessing and model training strategies, the generalization ability and stability of the proposed CI prediction model were significantly enhanced. Experimental results demonstrate that, when using three physiological parameters as input, the ANN achieved a classification accuracy of 97.78%, substantially outperforming traditional approaches. Even under two-parameter input conditions, the model maintained strong predictive performance. These findings confirm the effectiveness and practical potential of the proposed framework for real-time, non-invasive CI assessment. Moreover, this research has received rigorous assessment and approval from the Institutional Review Board (IRB) under application number 202501987B0. Full article

(This article belongs to the Special Issue Artificial Intelligence Revolution in Biomedical Image and Signal Processing: Innovations and Applications)

► Show Figures

Figure 1

16 pages, 4004 KB

Open AccessArticle

Automated Aortic Quantification Based on Artificial Intelligence: Validation Using Contrast-Enhanced and Non-Contrast CT Scans from the Same Session

by Jia-Sheng Hong, Yun-Hsuan Tzeng, Kuan-Ting Wu, Shih-Yu Huang, Ting-Wei Wang, Guan-Yu Li, Chun-Yi Lin, Ho-Ren Liu, Hai-Neng Fu, Yung-Tsai Lee, Wei-Hsian Yin and Yu-Te Wu

Bioengineering 2026, 13(4), 471; https://doi.org/10.3390/bioengineering13040471 - 17 Apr 2026

Viewed by 687

Abstract

Early detection of aortic dilatation is clinically important for preventing progression to serious aortic disease and enabling timely intervention. We aimed to develop an AI method for quantifying the aorta in both contrast-enhanced and non-contrast CT scans, assisting early detection of aortic dilation. A total of 190 patient cases were analyzed, each having paired contrast-enhanced and non-contrast CT scans acquired in the same session, resulting in 380 scans. Our approach, based on open-source tools, demonstrated strong agreement with manual annotations, particularly in the ascending aorta. For contrast-enhanced CT, the AI achieved a correlation coefficient of 0.987 and intraclass correlation coefficient (ICC) of 0.986; for non-contrast CT, both were 0.945. Compared with clinical records, the sensitivity of AI detection was 97% for contrast-enhanced CT and 94% for non-contrast CT. This AI-based workflow enables highly sensitive automated aortic quantification in both contrast-enhanced and non-contrast CT scans, supporting broader clinical applicability across different imaging conditions. Full article

(This article belongs to the Special Issue Artificial Intelligence Revolution in Biomedical Image and Signal Processing: Innovations and Applications)

► Show Figures

Figure 1

15 pages, 5052 KB

Open AccessArticle

Automated Collateral Classification on CT Angiography in Acute Ischemic Stroke: Performance Trends Across Hyperparameter Combinations

by Chi-Ming Ku and Tzong-Rong Ger

Bioengineering 2026, 13(1), 124; https://doi.org/10.3390/bioengineering13010124 - 21 Jan 2026

Viewed by 1084

Abstract

Collateral status is an important therapeutic indicator for acute ischemic stroke (AIS), yet visual collateral grading remains subjective and suffers from inter-observer variability. To address this limitation, this study automatically extracted binarized vascular morphological features from CTA images and developed a convolutional neural network (CNN) for automated collateral classification. Performance trends were systematically analyzed across diverse hyperparameter combinations to meet different clinical decision needs. A total of 157 AIS patients (median age 65 [57–74] years; 61.8% were male) were retrospectively enrolled and stratified by Menon score into good (3–5, n = 117) and poor (0–2, n = 40) collateral groups. A total of 192 architectures were established, and three representative model tendencies emerged: a sensitivity-oriented model (AUC = 0.773; sensitivity = 87.18%; specificity = 65.00%), a balanced model (AUC = 0.768; sensitivity = 72.65%; specificity = 77.50%), and a specificity-oriented model (AUC = 0.753; sensitivity = 63.25%; specificity = 85.00%). These results demonstrate that kernel size, the number of filters in the first layer, and the number of convolutional layers are key determinants of performance directionality, allowing tailored model selection depending on clinical requirements. This work highlights the feasibility of CTA-based automated collateral classification and provides a systematic framework for developing models optimized for sensitivity, specificity, or balanced decision-making. The findings may serve as a reference for clinical model deployment and have potential for integration into multi-objective AI systems for endovascular thrombectomy patient triage. Full article

(This article belongs to the Special Issue Artificial Intelligence Revolution in Biomedical Image and Signal Processing: Innovations and Applications)

► Show Figures

Graphical abstract

10 pages, 511 KB

Open AccessArticle

Improving Benign and Malignant Classifications in Mammography with ROI-Stratified Deep Learning

by Kenji Yoshitsugu, Kazumasa Kishimoto and Tadamasa Takemura

Bioengineering 2025, 12(8), 885; https://doi.org/10.3390/bioengineering12080885 - 20 Aug 2025

Cited by 3 | Viewed by 2026

Abstract

Deep learning has achieved widespread adoption for medical image diagnosis, with extensive research dedicated to mammographic image analysis for breast cancer screening. This study investigates the hypothesis that incorporating region-of-interest (ROI) mask information for individual mammographic images during deep learning can improve the accuracy of benign/malignant diagnoses. Swin Transformer and ConvNeXtV2 deep learning models were used to evaluate their performance on the public VinDr and CDD-CESM datasets. Our approach involved stratifying mammographic images based on the presence or absence of ROI masks, performing independent training and prediction for each subgroup, and subsequently merging the results. Baseline prediction metrics (sensitivity, specificity, F-score, and accuracy) without ROI-stratified separation were the following: VinDr/Swin Transformer (0.00, 1.00, 0.00, 0.85), VinDr/ConvNeXtV2 (0.00, 1.00, 0.00, 0.85), CDD-CESM/Swin Transformer (0.29, 0.68, 0.41, 0.48), and CDD-CESM/ConvNeXtV2 (0.65, 0.65, 0.65, 0.65). Subsequent analysis with ROI-stratified separation demonstrated marked improvements in these metrics: VinDr/Swin Transformer (0.93, 0.87, 0.90, 0.87), VinDr/ConvNeXtV2 (0.90, 0.86, 0.88, 0.87), CDD-CESM/Swin Transformer (0.65, 0.65, 0.65, 0.65), and CDD-CESM/ConvNeXtV2 (0.74, 0.61, 0.67, 0.68). These findings provide compelling evidence that validate our hypothesis and affirm the utility of considering ROI mask information for enhanced diagnostic accuracy in mammography. Full article

(This article belongs to the Special Issue Artificial Intelligence Revolution in Biomedical Image and Signal Processing: Innovations and Applications)

► Show Figures

Figure 1

11 pages, 3678 KB

Open AccessArticle

Plug-and-Play Self-Supervised Denoising for Pulmonary Perfusion MRI

by Changyu Sun, Yu Wang, Cody Thornburgh, Ai-Ling Lin, Kun Qing, John P. Mugler III and Talissa A. Altes

Bioengineering 2025, 12(7), 724; https://doi.org/10.3390/bioengineering12070724 - 1 Jul 2025

Viewed by 1725

Abstract

Pulmonary dynamic contrast-enhanced (DCE) MRI is clinically useful for assessing pulmonary perfusion, but its signal-to-noise ratio (SNR) is limited. A self-supervised learning network-based plug-and-play (PnP) denoising model was developed to improve the image quality of pulmonary perfusion MRI. A dataset of patients with suspected pulmonary diseases was used. Asymmetric pixel-shuffle downsampling blind-spot network (AP-BSN) training inputs were two-dimensional background-subtracted perfusion images without clean ground truth. The AP-BSN is incorporated into a PnP model (PnP-BSN) for balancing noise control and image fidelity. Model performance was evaluated by SNR, sharpness, and overall image quality from two radiologists. The fractal dimension and k-means segmentation of the pulmonary perfusion images were calculated for comparing denoising performance. The model was trained on 29 patients and tested on 8 patients. The performance of PnP-BSN was compared to denoising convolutional neural network (DnCNN) and a Gaussian filter. PnP-BSN showed the highest reader scores in terms of SNR, sharpness, and overall image quality as scored by two radiologists. The expert scoring results for DnCNN, Gaussian, and PnP-BSN were 2.25 ± 0.65, 2.44 ± 0.73, and 3.56 ± 0.73 for SNR; 2.62 ± 0.52, 2.62 ± 0.52, and 3.38 ± 0.64 for sharpness; and 2.16 ± 0.33, 2.34 ± 0.42, and 3.53 ± 0.51 for overall image quality (p < 0.05 for all). PnP-BSN outperformed DnCNN and a Gaussian filter for denoising pulmonary perfusion MRI, which led to improved quantitative fractal analysis. Full article

(This article belongs to the Special Issue Artificial Intelligence Revolution in Biomedical Image and Signal Processing: Innovations and Applications)

► Show Figures

Figure 1

26 pages, 6715 KB

Open AccessArticle

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

Cited by 1 | Viewed by 5030

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) 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

(This article belongs to the Special Issue Artificial Intelligence Revolution in Biomedical Image and Signal Processing: Innovations and Applications)

► Show Figures

Figure 1

22 pages, 8768 KB

Open AccessArticle

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

Cited by 7 | Viewed by 3603

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 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

(This article belongs to the Special Issue Artificial Intelligence Revolution in Biomedical Image and Signal Processing: Innovations and Applications)

► Show Figures

Graphical abstract

Other

Jump to: Research

21 pages, 1558 KB

Open AccessSystematic Review

From Echocardiography to CT/MRI: Lessons for AI Implementation in Cardiovascular Imaging in LMICs—A Systematic Review and Narrative Synthesis

by Ahmed Marey, Saba Mehrtabar, Ahmed Afify, Basudha Pal, Arcadia Trvalik, Sola Adeleke and Muhammad Umair

Bioengineering 2025, 12(10), 1038; https://doi.org/10.3390/bioengineering12101038 - 27 Sep 2025

Cited by 8 | Viewed by 2459

Abstract

Objectives: The aim of this study was to synthesize current evidence on artificial intelligence (AI) adoption in cardiovascular imaging across low- and middle-income countries (LMICs), highlighting diagnostic performance, implementation barriers, and potential solutions. Methods: We conducted a systematic review of PubMed, Embase, Cochrane Library, Web of Science, and Scopus for studies evaluating AI-based echocardiography, cardiac CT, or cardiac MRI in LMICs. Articles were screened according to PRISMA guidelines, and data on diagnostic outcomes, challenges, and enabling factors were extracted and narratively synthesized. Results: Twelve studies met the inclusion criteria. AI-driven methods frequently surpassed 90% accuracy in detecting coronary artery disease, rheumatic heart disease, and left ventricular hypertrophy, often enabling task shifting to non-expert operators. Challenges included limited dataset diversity, operator dependence, infrastructure constraints, and ethical considerations. Insights from high-income countries, such as automated segmentation and accelerated imaging, suggest potential for broader AI integration in cardiac MRI and CT. Conclusions: AI holds promise for enhancing cardiovascular care in LMICs by improving diagnostic accuracy and workforce efficiency. However, multi-center data sharing, targeted training, reliable infrastructure, and robust governance are essential for sustainable adoption. This review underscores AI’s capacity to bridge resource gaps in LMICs, offering practical pathways for future research, clinical practice, and policy development in global cardiovascular imaging. Full article

(This article belongs to the Special Issue Artificial Intelligence Revolution in Biomedical Image and Signal Processing: Innovations and Applications)

► Show Figures

Journal Menu

Journal Browser

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

Share This Special Issue

Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (8 papers)

Research

Other

Further Information

Guidelines

MDPI Initiatives

Follow MDPI