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Biosensors
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AI-Enabled Biosensor Technologies for Boosting Medical Applications
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AI-Enabled Biosensor Technologies for Boosting Medical Applications

A special issue of Biosensors (ISSN 2079-6374). This special issue belongs to the section "Biosensor and Bioelectronic Devices".

Deadline for manuscript submissions: 1 August 2026 | Viewed by 1648

Special Issue Editors

Dr. Carlos G. Juan

E-Mail Website
Guest Editor
Institute of Bioengineering, Miguel Hernández University of Elche, 03202 Elche, Spain
Interests: microwave sensors; glucose sensors; resonators; quality factor; dielectric characterization; non-invasive biosensors; electromagnetic fields
Special Issues, Collections and Topics in MDPI journals
Dr. José María Vicente-Samper

E-Mail Website
Guest Editor
Department of Civil Engineering, University of Alicante, San Vicente del Raspeig, Spain
Interests: biomedical engineering; electronics; monitoring devices; machine learning; robotics; automation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biosensing technologies find a myriad of applications in many aspects of modern society. Among all these, its quintessential use in medical, biological and biomedical contexts has been actively investigated and exploited during the last decades. The range of available biosensor techniques is wide, from biochemical sensors to reagent, ultrasound, electronic, optic, optoelectronic, or electromagnetic sensors, and show promising potential for unprecedented advancements. The development of novel biosensing technologies for future medical applications is therefore considerably alluring.

In the current times, researchers are starting to master all the aspects related to the physical implementation of biosensors with different technologies. Current science provides effective solutions for challenging areas such as sensitive and selective measurement, efficient implementation, optimized design, wearable and implantable devices, powering, data sensing and recording, and transmission and reading. Consequently, the main limitations of the final applications are seen in data processing or interpretation. It is in this context that modern artificial intelligence (AI) techniques rise to potentially add the finishing touches. Their pattern recognition and data processing capabilities are unmatched, and they open the door to previously unthinkable applications for these technologies.

In this multidisciplinary topical collection, we seek to compile insightful contributions that explore the combinations and possible synergies of these two disciplines—biosensors and AI processing techniques—with appealing future prospects for the biomedical realm. We anticipate novel ideas, innovative devices, and cutting-edge approaches that will expand the current boundaries of scientific knowledge. Prospective researchers are encouraged to submit their original works and comprehensive review articles, contributing to the advancement of this thrilling research field. By harnessing the convergence of biosensors and AI, we expect ground-breaking advancements that address critical challenges within the medical and biomedical domains. Join us in this Special Issue as we propel the future of healthcare towards unprecedented technological enhancement.

You may choose our Joint Special Issue in Sensors.

Dr. Carlos G. Juan
Dr. José María Vicente-Samper
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. Biosensors 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 2200 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

  • biosensors
  • reagents
  • chemical sensors
  • electronic sensors
  • optical sensors
  • electromagnetic sensors
  • biomedical applications
  • artificial intelligence
  • deep learning
  • machine learning
  • medical imaging

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Published Papers (2 papers)

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18 pages, 2879 KiB  
Article
Smartphone-Compatible Colorimetric Detection of CA19-9 Using Melanin Nanoparticles and Deep Learning
by Turgut Karademir, Gizem Kaleli-Can and Başak Esin Köktürk-Güzel
Biosensors 2025, 15(8), 507; https://doi.org/10.3390/bios15080507 - 5 Aug 2025
Viewed by 372
Abstract
Paper-based colorimetric biosensors represent a promising class of low-cost diagnostic tools that do not require external instrumentation. However, their broader applicability is limited by the environmental concerns associated with conventional metal-based nanomaterials and the subjectivity of visual interpretation. To address these challenges, this [...] Read more.
Paper-based colorimetric biosensors represent a promising class of low-cost diagnostic tools that do not require external instrumentation. However, their broader applicability is limited by the environmental concerns associated with conventional metal-based nanomaterials and the subjectivity of visual interpretation. To address these challenges, this study introduces a proof-of-concept platform—using CA19-9 as a model biomarker—that integrates naturally derived melanin nanoparticles (MNPs) with machine learning-based image analysis to enable environmentally sustainable and analytically robust colorimetric quantification. Upon target binding, MNPs induce a concentration-dependent color transition from yellow to brown. This visual signal was quantified using a machine learning pipeline incorporating automated region segmentation and regression modeling. Sensor areas were segmented using three different algorithms, with the U-Net model achieving the highest accuracy (average IoU: 0.9025 ± 0.0392). Features extracted from segmented regions were used to train seven regression models, among which XGBoost performed best, yielding a Mean Absolute Percentage Error (MAPE) of 17%. Although reduced sensitivity was observed at higher analyte concentrations due to sensor saturation, the model showed strong predictive accuracy at lower concentrations, which are especially challenging for visual interpretation. This approach enables accurate, reproducible, and objective quantification of colorimetric signals, thereby offering a sustainable and scalable alternative for point-of-care diagnostic applications. Full article
(This article belongs to the Special Issue AI-Enabled Biosensor Technologies for Boosting Medical Applications)
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17 pages, 13507 KiB  
Article
Molecular Association Assay Systems for Imaging Protein–Protein Interactions in Mammalian Cells
by Sung-Bae Kim, Tadaomi Furuta, Suresh Thangudu, Arutselvan Natarajan and Ramasamy Paulmurugan
Biosensors 2025, 15(5), 299; https://doi.org/10.3390/bios15050299 - 8 May 2025
Viewed by 541
Abstract
Molecular imaging probes play a pivotal role in assaying molecular events in various physiological systems. In this study, we demonstrate a new genre of bioluminescent probes for imaging protein–protein interactions (PPIs) in mammalian cells, named the molecular association assay (MAA) probe. The MAA [...] Read more.
Molecular imaging probes play a pivotal role in assaying molecular events in various physiological systems. In this study, we demonstrate a new genre of bioluminescent probes for imaging protein–protein interactions (PPIs) in mammalian cells, named the molecular association assay (MAA) probe. The MAA probe is designed to be as simple as a full-length marine luciferase fused to a protein of interest with a flexible linker. This simple fusion protein alone surprisingly works by recognizing a specific ligand, interacting with a counterpart protein of the PPI, and developing bioluminescence (BL) in mammalian cells. We made use of an artificial intelligence (AI) tool to simulate the binding modes and working mechanisms. Our AlphaFold-based analysis on the binding mode suggests that the hinge region of the MAA probe is flexible before ligand binding but becomes stiff after ligand binding and protein association. The sensorial properties of representative MAA probes, FRB-ALuc23 and FRB-R86SG, are characterized with respect to the quantitative feature, BL spectrum, and in vivo tumor imaging using xenografted mice. Our AI-based simulation of the working mechanisms reveals that the association of MAA probes with the other proteins works in a way to facilitate the substrate’s access to the active sites of the luciferase (ALuc23 or R86SG). We prove that the concept of MAA is generally applicable to other examples, such as the ALuc16- or R86SG-fused estrogen receptor ligand-binding domain (ER LBD). Considering the versatility of this conceptionally unique and distinctive molecular imaging probe compared to conventional ones, we are expecting the widespread application of these probes as a new imaging repertoire to determine PPIs in living organisms. Full article
(This article belongs to the Special Issue AI-Enabled Biosensor Technologies for Boosting Medical Applications)
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