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Advances in Biomedical Imaging and Sensing: Technologies, Applications, and Future Directions

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: 28 February 2026 | Viewed by 3226

Special Issue Editors


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Guest Editor
Department of Computer Engineering, Gachon University, Seongnam 13120, Republic of Korea
Interests: machine learning; computer vision; performance optimization; medical imaging; explainable AI

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Guest Editor
Department of Physics, Charles E. Schmidt College of Science, Florida Atlantic University, Boca Raton, FL 33431-0991, USA
Interests: data mining; big data analytics; machine learning; AI in healthcare
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Science and Technology, The University of New England, Armidale, NSW 2350, Australia
Interests: artificial intelligence; machine learning; multi-agent systems; project management

Special Issue Information

Dear Colleagues,

Recent advances in biomedical imaging and sensing technologies have revolutionized healthcare by enabling non-invasive visualization and the precise quantification of physiological and pathological processes. These innovations play a pivotal role in disease diagnosis, treatment planning, and therapeutic monitoring.

This Special Issue of Sensors, titled "Advances in Biomedical Imaging and Sensing: Technologies, Applications, and Future Directions", invites original research articles, reviews, and short communications that contribute to the development and application of advanced imaging and sensing techniques in biomedical contexts. We aim to highlight cutting-edge research on imaging modalities (such as X-Rays, MRI, CT, PET, ultrasound, optical coherence tomography, and photoacoustic imaging) and sensing systems integrated with AI, robotics, or novel materials. Topics related to the development of imaging sensors, image reconstruction algorithms, data fusion strategies, deep learning frameworks, and real-time diagnostic tools are particularly welcome. Contributions that bridge the gap between engineering and clinical applications—ranging from early-stage prototypes to validated clinical tools—are encouraged.

Topics of interest include, but are not limited to, the following:

  • Magnetic resonance imaging (MRI) and computed tomography (CT);
  • Optical imaging and photoacoustic imaging;
  • Ultrasound and elastography imaging;
  • Positron emission tomography (PET) and SPECT;
  • Multimodal and hybrid imaging systems;
  • Smart biosensors and imaging sensors;
  • Ai and deep learning for biomedical image analysis;
  • Real-time medical image reconstruction;
  • Biomedical signal and image fusion;
  • Medical image segmentation, registration, and classification;
  • Image-guided interventions and therapy;
  • Wearable imaging devices and portable sensors;
  • Novel sensor materials and imaging hardware designs.

We welcome contributions from both academia and industry that address theoretical advances, practical applications, and case studies with real-world implications in biomedical imaging and sensing.

Dr. Rizwan Ali Naqvi
Dr. Abbas Jafar
Dr. Wazir Muhammad
Dr. Fareed Ud Din
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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly 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 2600 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
  • magnetic resonance imaging (MRI)
  • computed tomography (CT)
  • biomedical signal processing

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Published Papers (2 papers)

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20 pages, 3247 KB  
Article
Repeatability of Corneal Astigmatism and Equivalent Power with the MS-39 Tomographer Derived from Model Surface Fitting in a Cataractous Population
by Achim Langenbucher, Nóra Szentmáry, Alan Cayless, Muntadher Al Karam, Peter Hoffmann, Theo G. Seiler and Jascha Wendelstein
Sensors 2025, 25(19), 6171; https://doi.org/10.3390/s25196171 - 5 Oct 2025
Viewed by 540
Abstract
We investigated the repeatability of the MS-39 in determining power vector components—the spherical equivalent (SEQ) and astigmatic powers (C0 and C45) and asphericity (Q)—of corneal epithelium, stroma, and endothelium in a large patient cohort. In this retrospective cross-sectional single-centre study, we evaluated a [...] Read more.
We investigated the repeatability of the MS-39 in determining power vector components—the spherical equivalent (SEQ) and astigmatic powers (C0 and C45) and asphericity (Q)—of corneal epithelium, stroma, and endothelium in a large patient cohort. In this retrospective cross-sectional single-centre study, we evaluated a dataset containing 600 MS-39 anterior segment tomography measurements from 200 eyes (three repeat measurements each) taken prior to cataract surgery. The exported measurements included height map data for the epithelium, stroma, and endothelium surface. Model surfaces (spherocylinder (SphCyl), cylindrical conoid (CylConoid), and biconic (Biconic), all in the 3/6 mm zone) were fitted using nonlinear iterative optimisation, minimising the height difference between the measurement and model. The mean (MEAN) and standard deviation (SD) for each sequence of measurements were derived and analysed. In the 3 mm and 6 mm zone, the MEAN SEQ was 53.47/53.56/53.57 and 53.21/53.54/53.54 D for SphCyl/CylConoid/Biconic for the epithelium, −4.47/−4.51/−4.51 and −4.45/−4.50/−4.50 D for the stroma, and −6.23/−6.26/−6.26 and −6.18/−6.29/−6.30 D for the endothelium. With the three surface models and the 3/6 mm zone, the SD for SEQ/C0/C45 was in the range of 0.04 to 0.11/0.05 to 0.13/0.04 to 0.11 D for epithelium; 0.01 to 0.02/0.01 to 0.05/0.01 to 0.06 D for stroma; and 0.01 to 0.02/0.02 to 0.07/0.03 to 0.07 D for endothelium. Fitting floating model surfaces with astigmatism to map data of the corneal epithelium, stroma, and endothelium seems to be a robust and reliable method for extracting equivalent power and astigmatism using all the datapoints within a region of interest. Full article
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31 pages, 1368 KB  
Systematic Review
eXplainable Artificial Intelligence (XAI): A Systematic Review for Unveiling the Black Box Models and Their Relevance to Biomedical Imaging and Sensing
by Nadeesha Hettikankanamage, Niusha Shafiabady, Fiona Chatteur, Robert M. X. Wu, Fareed Ud Din and Jianlong Zhou
Sensors 2025, 25(21), 6649; https://doi.org/10.3390/s25216649 - 30 Oct 2025
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Abstract
Artificial Intelligence (AI) has achieved immense progress in recent years across a wide array of application domains, with biomedical imaging and sensing emerging as particularly impactful areas. However, the integration of AI in safety-critical fields, particularly biomedical domains, continues to face a major [...] Read more.
Artificial Intelligence (AI) has achieved immense progress in recent years across a wide array of application domains, with biomedical imaging and sensing emerging as particularly impactful areas. However, the integration of AI in safety-critical fields, particularly biomedical domains, continues to face a major challenge of explainability arising from the opacity of complex prediction models. Overcoming this obstacle falls within the realm of eXplainable Artificial Intelligence (XAI), which is widely acknowledged as an essential aspect for successfully implementing and accepting AI techniques in practical applications to ensure transparency, fairness, and accountability in the decision-making processes and mitigate potential biases. This article provides a systematic cross-domain review of XAI techniques applied to quantitative prediction tasks, with a focus on their methodological relevance and potential adaptation to biomedical imaging and sensing. To achieve this, following PRISMA guidelines, we conducted an analysis of 44 Q1 journal articles that utilised XAI techniques for prediction applications across different fields where quantitative databases were used, and their contributions to explaining the predictions were studied. As a result, 13 XAI techniques were identified for prediction tasks. Shapley Additive eXPlanations (SHAP) was identified in 35 out of 44 articles, reflecting its frequent computational use for feature-importance ranking and model interpretation. Local Interpretable Model-Agnostic Explanations (LIME), Partial Dependence Plots (PDPs), and Permutation Feature Index (PFI) ranked second, third, and fourth in popularity, respectively. The study also recognises theoretical limitations of SHAP and related model-agnostic methods, such as their additive and causal assumptions, which are particularly critical in heterogeneous biomedical data. Furthermore, a synthesis of the reviewed studies reveals that while many provide computational evaluation of explanations, none include structured human–subject usability validation, underscoring an important research gap for clinical translation. Overall, this study offers an integrated understanding of quantitative XAI techniques, identifies methodological and usability gaps for biomedical adaptation, and provides guidance for future research aimed at safe and interpretable AI deployment in biomedical imaging and sensing. Full article
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