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Bioengineering
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AI Advancements in Healthcare: Medical Imaging and Sensing Technologies
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AI Advancements in Healthcare: Medical Imaging and Sensing Technologies

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 4386

Special Issue Editors

Dr. Mohammed A. Al-masni

E-Mail Website
Guest Editor
Department of Artificial Intelligence and Data Science, College of AI Convergence, Sejong University, Seoul, Republic of Korea
Interests: medical image analysis; artificial intelligence; deep learning; abnormalities segmentation and diagnosis; biomedical image/signal processing; image synthesis; MRI motion artifacts correction
Dr. Kanghyun Ryu

E-Mail Website
Guest Editor
Artificial Intelligence and Robotics Institute, Korea Institute of Science and Technology, Seoul, Republic of Korea
Interests: medical image reconstruction; medical image synthesis; image segmentation; medical image analysis

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) has recently revolutionized healthcare with advancements in medical imaging and sensing technologies. These advancements result in automated, precise, and efficient diagnosis and prognosis tools, significantly improving disease detection and patient care. AI algorithms demonstrate exceptional proficiency in analyzing medical images (MRI, CT, PET, etc.) and signals (EEG, ECG, EMG) for the classification of abnormalities, as well as the detection and segmentation of suspicious regions. This improves diagnostic accuracy, expedites decision-making processes, and offers benefits across various medical specialties. Moreover, researchers are actively addressing challenges such as artifact correction, image synthesis, and multi-modality registration to enhance medical data analysis, leading to more reliable clinical decisions and treatment plans. The integration of AI with medical imaging and sensing presents vast potential. It enables early disease detection, personalized treatment plans, and the enhanced monitoring of various conditions. With ongoing advancements in AI and computing capabilities, the potential for further innovation in healthcare is boundless, promising a future where healthcare delivery is more precise, efficient, and patient-centric than ever before.

This Special Issue invites novel research and technical advancements in biomedical imaging and sensing technologies. Original research papers and comprehensive reviews focusing on cutting-edge methodologies are encouraged.

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

  • Biomedical imaging;
  • Biosignals;
  • Medical image analysis;
  • Abnormalities classification and detection;
  • Medical image segmentation;
  • Medical image reconstruction;
  • Medical image denoising;
  • Medical image registration;
  • AI in biomedical systems;
  • Computer-aided diagnosis systems.

Dr. Mohammed A. Al-masni
Dr. Kanghyun Ryu
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 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

  • biomedical imaging
  • biosignals
  • medical image analysis
  • abnormalities classification and detection
  • medical image segmentation
  • medical image reconstruction
  • medical image denoising
  • medical image registration
  • AI in biomedical systems
  • computer-aided diagnosis systems

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

Published Papers (3 papers)

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Research

20 pages, 11903 KiB  
Article
Regional Brain Aging Disparity Index: Region-Specific Brain Aging State Index for Neurodegenerative Diseases and Chronic Disease Specificity
by Yutong Wu, Shen Sun, Chen Zhang, Xiangge Ma, Xinyu Zhu, Yanxue Li, Lan Lin and Zhenrong Fu
Bioengineering 2025, 12(6), 607; https://doi.org/10.3390/bioengineering12060607 - 3 Jun 2025
Viewed by 468
Abstract
This study proposes a novel brain-region-level aging assessment paradigm based on Shapley value interpretation, aiming to overcome the interpretability limitations of traditional brain age prediction models. Although deep-learning-based brain age prediction models using neuroimaging data have become crucial tools for evaluating abnormal brain [...] Read more.
This study proposes a novel brain-region-level aging assessment paradigm based on Shapley value interpretation, aiming to overcome the interpretability limitations of traditional brain age prediction models. Although deep-learning-based brain age prediction models using neuroimaging data have become crucial tools for evaluating abnormal brain aging, their unidimensional brain age–chronological age discrepancy metric fails to characterize the regional heterogeneity of brain aging. Meanwhile, despite Shapley additive explanations having demonstrated potential for revealing regional heterogeneity, their application in complex deep learning algorithms has been hindered by prohibitive computational complexity. To address this, we innovatively developed a computational framework featuring efficient Shapley value approximation through a novel multi-stage computational strategy that significantly reduces complexity, thereby enabling an interpretable analysis of deep learning models. By establishing a reference system based on standard Shapley values from healthy populations, we constructed an anatomically specific Regional Brain Aging Deviation Index (RBADI) that maintains age-related validity. Experimental validation using UK Biobank data demonstrated that our framework successfully identified the thalamus (THA) and hippocampus (HIP) as core contributors to brain age prediction model decisions, highlighting their close associations with physiological aging. Notably, it revealed significant correlations between the insula (INS) and alcohol consumption, as well as between the inferior frontal gyrus opercular part (IFGoperc) and smoking history. Crucially, the RBADI exhibited superior performance in the tri-class classification of prodromal neurodegenerative diseases (HCs vs. MCI vs. AD: AUC = 0.92; HCs vs. pPD vs. PD: AUC = 0.86). This framework not only enables the practical implementation of Shapley additive explanations in brain age prediction deep learning models but also establishes anatomically interpretable biomarkers. These advancements provide a novel spatial analytical dimension for investigating brain aging mechanisms and demonstrate significant clinical translational value for early neurodegenerative disease screening, ultimately offering a new methodological tool for deciphering the neural mechanisms of aging. Full article
(This article belongs to the Special Issue AI Advancements in Healthcare: Medical Imaging and Sensing Technologies)
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31 pages, 3939 KiB  
Article
CAD-Skin: A Hybrid Convolutional Neural Network–Autoencoder Framework for Precise Detection and Classification of Skin Lesions and Cancer
by Abdullah Khan, Muhammad Zaheer Sajid, Nauman Ali Khan, Ayman Youssef and Qaisar Abbas
Bioengineering 2025, 12(4), 326; https://doi.org/10.3390/bioengineering12040326 - 21 Mar 2025
Viewed by 890
Abstract
Skin cancer is a class of disorder defined by the growth of abnormal cells on the body. Accurately identifying and diagnosing skin lesions is quite difficult because skin malignancies share many common characteristics and a wide range of morphologies. To face this challenge, [...] Read more.
Skin cancer is a class of disorder defined by the growth of abnormal cells on the body. Accurately identifying and diagnosing skin lesions is quite difficult because skin malignancies share many common characteristics and a wide range of morphologies. To face this challenge, deep learning algorithms have been proposed. Deep learning algorithms have shown diagnostic efficacy comparable to dermatologists in the discipline of images-based skin lesion diagnosis in recent research articles. This work proposes a novel deep learning algorithm to detect skin cancer. The proposed CAD-Skin system detects and classifies skin lesions using deep convolutional neural networks and autoencoders to improve the classification efficiency of skin cancer. The CAD-Skin system was designed and developed by the use of the modern preprocessing approach, which is a combination of multi-scale retinex, gamma correction, unsharp masking, and contrast-limited adaptive histogram equalization. In this work, we have implemented a data augmentation strategy to deal with unbalanced datasets. This step improves the model’s resilience to different pigmented skin conditions and avoids overfitting. Additionally, a Quantum Support Vector Machine (QSVM) algorithm is integrated for final-stage classification. Our proposed CAD-Skin enhances category recognition for different skin disease severities, including actinic keratosis, malignant melanoma, and other skin cancers. The proposed system was tested using the PAD-UFES-20-Modified, ISIC-2018, and ISIC-2019 datasets. The system reached accuracy rates of 98%, 99%, and 99%, consecutively, which is higher than state-of-the-art work in the literature. The minimum accuracy achieved for certain skin disorder diseases reached 97.43%. Our research study demonstrates that the proposed CAD-Skin provides precise diagnosis and timely detection of skin abnormalities, diversifying options for doctors and enhancing patient satisfaction during medical practice. Full article
(This article belongs to the Special Issue AI Advancements in Healthcare: Medical Imaging and Sensing Technologies)
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25 pages, 12688 KiB  
Article
Gait Impairment Analysis Using Silhouette Sinogram Signals and Assisted Knowledge Learning
by Mohammed A. Al-masni, Eman N. Marzban, Abobakr Khalil Al-Shamiri, Mugahed A. Al-antari, Maali Ibrahim Alabdulhafith, Noha F. Mahmoud, Nagwan Abdel Samee and Yasser M. Kadah
Bioengineering 2024, 11(5), 477; https://doi.org/10.3390/bioengineering11050477 - 10 May 2024
Viewed by 1978
Abstract
The analysis of body motion is a valuable tool in the assessment and diagnosis of gait impairments, particularly those related to neurological disorders. In this study, we propose a novel automated system leveraging artificial intelligence for efficiently analyzing gait impairment from video-recorded images. [...] Read more.
The analysis of body motion is a valuable tool in the assessment and diagnosis of gait impairments, particularly those related to neurological disorders. In this study, we propose a novel automated system leveraging artificial intelligence for efficiently analyzing gait impairment from video-recorded images. The proposed methodology encompasses three key aspects. First, we generate a novel one-dimensional representation of each silhouette image, termed a silhouette sinogram, by computing the distance and angle between the centroid and each detected boundary points. This process enables us to effectively utilize relative variations in motion at different angles to detect gait patterns. Second, a one-dimensional convolutional neural network (1D CNN) model is developed and trained by incorporating the consecutive silhouette sinogram signals of silhouette frames to capture spatiotemporal information via assisted knowledge learning. This process allows the network to capture a broader context and temporal dependencies within the gait cycle, enabling a more accurate diagnosis of gait abnormalities. This study conducts training and an evaluation utilizing the publicly accessible INIT GAIT database. Finally, two evaluation schemes are employed: one leveraging individual silhouette frames and the other operating at the subject level, utilizing a majority voting technique. The outcomes of the proposed method showed superior enhancements in gait impairment recognition, with overall F1-scores of 100%, 90.62%, and 77.32% when evaluated based on sinogram signals, and 100%, 100%, and 83.33% when evaluated based on the subject level, for cases involving two, four, and six gait abnormalities, respectively. In conclusion, by comparing the observed locomotor function to a conventional gait pattern often seen in healthy individuals, the recommended approach allows for a quantitative and non-invasive evaluation of locomotion. Full article
(This article belongs to the Special Issue AI Advancements in Healthcare: Medical Imaging and Sensing Technologies)
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