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Biosensors
Special Issues
Advanced Optical Imaging Biosensors: Technologies and Biomedical Applications
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Advanced Optical Imaging Biosensors: Technologies and Biomedical Applications

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

Deadline for manuscript submissions: 30 April 2026 | Viewed by 1482

Special Issue Editors

Dr. Wei Yan

E-Mail Website
Guest Editor
Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, College of Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China
Interests: optical super-resolution imaging; fluorescence lifetime imaging system; adaptive optics
Special Issues, Collections and Topics in MDPI journals
Dr. Yong Guo

E-Mail Website
Guest Editor
College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen, China
Interests: fluorescence microscopic imaging/volumetric imaging; fluorescence lifetime imaging
Dr. Xiao Peng

E-Mail Website
Guest Editor
College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen, China
Interests: biophotonics

Special Issue Information

Dear Colleagues,

In recent years, with the development of optical imaging techniques and probes, noval applications of optical imaging methods in biomedical and other related fields has been promoted, especially the effective combination of new optical imaging techniques and biosensors. Innovations in laser scanning confocal imaging, two-photon imaging, fluorescence lifetime imaging, super-resolution imaging, light sheet microscopy and multimodal imaging have explored human understanding of live cells and organisms, promoting rapid developments of biomedicine

This special issue aims to highlight novel biosensing strategies and imaging modalities, which may expand the boundaries of resolution, sensitivity, and spatiotemporal precision in biomedical research. The focus will be on imaging-based biosensors that provide unique insights into clinical diagnostics, drug screening, disease surveillance, and basic biological fields.

Dr. Wei Yan
Dr. Yong Guo
Dr. Xiao Peng
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. 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

  • optical microcavity biosensors
  • optical microring biosensors
  • photonic crystal biosensors
  • plasmonic biosensors
  • metasurface sensors
  • integrated photonic biosensors
  • optofluidic biochips
  • diagnosis
  • point-of-care
  • healthcare

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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

28 pages, 9838 KB  
Article
Evaluating the Performance of Hyperspectral Imaging Endoscopes: Mitigating Parameters Affecting Spectral Accuracy
by Siavash Mazdeyasna, Mohammed Shahriar Arefin, Andrew Fales, Silas J. Leavesley, T. Joshua Pfefer and Quanzeng Wang
Biosensors 2025, 15(11), 738; https://doi.org/10.3390/bios15110738 - 4 Nov 2025
Viewed by 688
Abstract
Hyperspectral imaging (HSI) is increasingly used in studies for medical applications as it provides both structural and functional information of biological tissue, enhancing diagnostic accuracy and clinical decision-making. Recently, HSI cameras (HSICs) have been integrated with medical endoscopes (HSIEs), capturing hypercube data beyond [...] Read more.
Hyperspectral imaging (HSI) is increasingly used in studies for medical applications as it provides both structural and functional information of biological tissue, enhancing diagnostic accuracy and clinical decision-making. Recently, HSI cameras (HSICs) have been integrated with medical endoscopes (HSIEs), capturing hypercube data beyond conventional white light imaging endoscopes. However, there are currently no cleared or approved HSIEs by the U.S. Food and Drug Administration (FDA). HSI accuracy depends on technologies and experimental parameters, which must be assessed for reliability. Importantly, the reflectance spectrum of a target can vary across different cameras and under different environmental or operational conditions. Thus, before reliable clinical translation can be achieved, a fundamental question must be addressed: can the same target yield consistent spectral measurements across different HSI systems and under varying acquisition conditions? This study investigates the impact of eight parameters—ambient light, exposure time, camera warm-up time, spatial and temporal averaging, camera focus, working distance, illumination angle, and target angle—on spectral measurements using two HSI techniques: interferometer-based spectral scanning and snapshot. Controlled experiments were conducted to evaluate how each parameter affects spectral accuracy and whether normalization can mitigate these effects. Our findings reveal that several parameters significantly influence spectral measurements, with some having a more pronounced impact. While normalization reduced variations for most parameters, it was less effective at mitigating errors caused by ambient light and camera warm-up time. Additionally, normalization did not eliminate spectral noise resulting from low exposure time, small region of interest, or a spectrally non-uniform light source. From these results, we propose practical considerations for optimizing HSI system performance. Implementing these measures can minimize variations in reflectance spectra of identical targets captured by different cameras and under diverse conditions, thereby supporting the reliable translation of HSI techniques to clinical applications. Full article
(This article belongs to the Special Issue Advanced Optical Imaging Biosensors: Technologies and Biomedical Applications)
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17 pages, 4347 KB  
Article
Visible-Light Hyperspectral Reconstruction and PCA-Based Feature Extraction for Malignant Pleural Effusion Cytology
by Chun-Liang Lai, Kun-Hua Lee, Hong-Thai Nguyen, Arvind Mukundan, Riya Karmakar, Tsung-Hsien Chen, Wen-Shou Lin and Hsiang-Chen Wang
Biosensors 2025, 15(11), 714; https://doi.org/10.3390/bios15110714 - 28 Oct 2025
Viewed by 638
Abstract
Malignant pleural effusion, commonly referred to as MPE, is a prevalent complication associated with individuals diagnosed with neoplastic disorders. The data acquired by pleural fluid cytology is beneficial for diagnostic objectives. Consequently, the initial step in the diagnostic procedure for lung cancer is [...] Read more.
Malignant pleural effusion, commonly referred to as MPE, is a prevalent complication associated with individuals diagnosed with neoplastic disorders. The data acquired by pleural fluid cytology is beneficial for diagnostic objectives. Consequently, the initial step in the diagnostic procedure for lung cancer is the analysis of pleural effusion fluid. This research aims to provide a cutting-edge model for analyzing PE cytology images. This model utilizes a computer-aided diagnosis (CAD) system that integrates hyperspectral imaging (HSI) technology for the classification of spectral variations. Giemsa, which is one of the most popular microscopic stains, is employed to stain the samples, after which a sensitive CCD mounted on a microscope captures the images. Subsequently, the HSI model is tasked with obtaining the image spectra. Principal Component Analysis (PCA) constitutes the concluding phase in the classification procedure of various cell types. We expect that the suggested technique will enable medical professionals to stage lung cancer more rapidly. In the future, we aspire to develop an extensive data system that utilizes deep learning techniques to facilitate the automatic classification of cells, thereby ensuring the most precise diagnosis. Furthermore, enhancing accuracy and minimizing data dimensions are important priorities to accelerate diagnostics, conserve resources, and reduce computing time. Full article
(This article belongs to the Special Issue Advanced Optical Imaging Biosensors: Technologies and Biomedical Applications)
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