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Biosensors
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Non-Invasive Biosensors for Clinical Diagnostics and Healthcare Monitoring
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Non-Invasive Biosensors for Clinical Diagnostics and Healthcare Monitoring

A special issue of Biosensors (ISSN 2079-6374). This special issue belongs to the section "Nano- and Micro-Technologies in Biosensors".

Deadline for manuscript submissions: closed (31 August 2025) | Viewed by 1775

Special Issue Editor

Dr. Pau Redón Lurbe

E-Mail Website
Guest Editor
Intelligent Clinical Algorithms, Analog Devices, Valencia, Spain
Interests: biosensors; biomarkers; algorithms; medical devices; cardiovascular; machine learning; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The design and development of biosensors with new or improved capabilities is an endlessly evolving field that has attracted interest and funding from academics and industrial partners. In the healthcare field, some of the key developments have focused on material science, biosensor design, and the development of algorithms responsible for guaranteeing their performance requirements over a broad range of working conditions.

The scientific challenges associated with each of the aforementioned fields coupled with continuously more demanding markets (e.g., more efficient and powerful biosensors) increase the complexity of innovation processes. While machine learning (ML) and artificial intelligence (AI) tools could contribute to accelerating and reducing costs, and democratizing knowledge, these tools have their challenges (e.g, unbalanced datasets, lack of annotated datasets, small databases, etc.) that must be taken into consideration for meaningful outcomes.  

This Special Issue aims to give readers an overview of the latest research conducted in the biosensor field (i.e., material science, biosensor design, and algorithm development). Papers presenting relevant scientific and technological advances as well as those on ML and AI tools are welcomed. Lastly, this Special Issue also welcomes papers that review the current ML and AI regulatory ecosystem, especially in the healthcare field. 

Dr. Pau Redón Lurbe
Guest Editor

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

  • non-invasive biosensors
  • clinical diagnostics
  • healthcare monitoring
  • machine learning (ML)
  • artificial intelligence (AI)

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

Published Papers (2 papers)

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Research

16 pages, 1228 KB  
Article
Simulation of an Asymmetric Photonic Structure Integrating Tamm Plasmon Polariton Modes and a Cavity Mode for Potential Urinary Glucose Sensing via Refractive Index Shifts
by Hung-Che Chou, Rashid G. Bikbaev, Ivan V. Timofeev, Mon-Juan Lee and Wei Lee
Biosensors 2025, 15(10), 644; https://doi.org/10.3390/bios15100644 - 29 Sep 2025
Viewed by 401
Abstract
Diabetes has become a global health challenge, driving the demand for innovative, non-invasive diagnostic technologies to improve glucose monitoring. Urinary glucose concentration, a reliable indicator of metabolic changes, provides a practical alternative for frequent monitoring without the discomfort of invasive methods. In this [...] Read more.
Diabetes has become a global health challenge, driving the demand for innovative, non-invasive diagnostic technologies to improve glucose monitoring. Urinary glucose concentration, a reliable indicator of metabolic changes, provides a practical alternative for frequent monitoring without the discomfort of invasive methods. In this simulation-based study, we propose a novel asymmetric photonic structure that integrates Tamm plasmon polariton (TPP) modes and a cavity mode for high-precision refractive index sensing, with a conceptual focus on the potential detection of urinary glucose. The structure supports three distinct resonance modes, each with unique field localization. Both the TPP modes, confined at the metallic–dielectric interfaces, serve as stable references whose wavelengths are unaffected by refractive-index variations in human urine, whereas the cavity mode exhibits a redshift with increasing refractive index, enabling high responsiveness to analyte changes. The evaluation of sensing performance employs a sensitivity formulation that leverages either TPP mode as a reference and the cavity mode as a probe, thereby achieving dependable measurement and spectral stability. The optimized design achieves a sensitivity of 693 nm·RIU−1 and a maximum figure of merit of 935 RIU−1, indicating high detection resolution and spectral sharpness. The device allows both reflectance and transmittance measurements to ensure enhanced versatility. Moreover, the coupling between TPP and cavity modes demonstrates hybrid resonance, empowering applications such as polarization-sensitive or angle-dependent filtering. The figure of merit is analyzed further, considering resonance wavelength shifts and spectral sharpness, thus manifesting the structure’s robustness. Although this study does not provide experimental data such as calibration curves, recovery rates, or specificity validation, the proposed structure offers a promising conceptual framework for refractive index-based biosensing in human urine. The findings position the structure as a versatile platform for advanced photonic systems, offering precision, tunability, and multifunctionality beyond the demonstrated optical sensing capabilities. Full article
(This article belongs to the Special Issue Non-Invasive Biosensors for Clinical Diagnostics and Healthcare Monitoring)
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19 pages, 3862 KB  
Article
Estimation of Total Hemoglobin (SpHb) from Facial Videos Using 3D Convolutional Neural Network-Based Regression
by Ufuk Bal, Faruk Enes Oguz, Kubilay Muhammed Sunnetci, Ahmet Alkan, Alkan Bal, Ebubekir Akkuş, Halil Erol and Ahmet Çağdaş Seçkin
Biosensors 2025, 15(8), 485; https://doi.org/10.3390/bios15080485 - 25 Jul 2025
Viewed by 1047
Abstract
Hemoglobin plays a critical role in diagnosing various medical conditions, including infections, trauma, hemolytic disorders, and Mediterranean anemia, which is particularly prevalent in Mediterranean populations. Conventional measurement methods require blood sampling and laboratory analysis, which are often time-consuming and impractical during emergency situations [...] Read more.
Hemoglobin plays a critical role in diagnosing various medical conditions, including infections, trauma, hemolytic disorders, and Mediterranean anemia, which is particularly prevalent in Mediterranean populations. Conventional measurement methods require blood sampling and laboratory analysis, which are often time-consuming and impractical during emergency situations with limited medical infrastructure. Although portable oximeters enable non-invasive hemoglobin estimation, they still require physical contact, posing limitations for individuals with circulatory or dermatological conditions. Additionally, reliance on disposable probes increases operational costs. This study presents a non-contact and automated approach for estimating total hemoglobin levels from facial video data using three-dimensional regression models. A dataset was compiled from 279 volunteers, with synchronized acquisition of facial video and hemoglobin values using a commercial pulse oximeter. After preprocessing, the dataset was divided into training, validation, and test subsets. Three 3D convolutional regression models, including 3D CNN, channel attention-enhanced 3D CNN, and residual 3D CNN, were trained, and the most successful model was implemented in a graphical interface. Among these, the residual model achieved the most favorable performance on the test set, yielding an RMSE of 1.06, an MAE of 0.85, and a Pearson correlation coefficient of 0.73. This study offers a novel contribution by enabling contactless hemoglobin estimation from facial video using 3D CNN-based regression techniques. Full article
(This article belongs to the Special Issue Non-Invasive Biosensors for Clinical Diagnostics and Healthcare Monitoring)
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