Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
9.8
5.6
Journals
Biosensors
Special Issues
Optical Biosensors for Healthcare: An Artificial Intelligence Approach
share announcement

Optical Biosensors for Healthcare: An Artificial Intelligence Approach

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

Deadline for manuscript submissions: 1 January 2027 | Viewed by 4482

Special Issue Editors

Dr. Shuo Chen

E-Mail Website
Guest Editor
School of Medicine and Bioinformatics Engineering, Northeastern University, Shenyang, China
Interests: biomedical imaging and sensing; biomedical spectroscopy; hyperspectral imaging; computational optical sensing and imaging; deep learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Min Guo

E-Mail Website
Guest Editor
State Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, China
Interests: fluorescence imaging; computational imaging; fluorescence probes; deep learning
Dr. Renzhe Bi

E-Mail Website
Guest Editor
A*STAR Skin Research Labs (A*SRL), Agency for Science, Technology and Research (A*STAR), 31 Biopolis Way, Singapore 138669, Singapore
Interests: biosensing and imaging; photoacoustics; Raman spectroscopy; diffuse optics; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advances in healthcare demand innovative technologies that enable rapid, accurate, and personalized diagnoses. Optical biosensors have emerged as a transformative tool, offering high sensitivity, real-time detection, and non-invasive monitoring of biomarkers for various diseases. In recent years, artificial intelligence (AI)-enhanced optical biosensors have grown into a rapidly advancing field, integrating state-of-the-art AI approaches into cutting-edge optical biosensor techniques to improve their performance and benefit outcomes in their healthcare applications. Following these AI advancements, notable progress has been made in optimal biosensor design, improved signal acquisition, transmission, and storage, along with more precise data analyses being instituted to fulfill the growing demand for optical biosensors in healthcare. This Special Issue aims to investigate how AI can redefine optical biosensor techniques to revolutionize healthcare applications, and encourages collaborations between engineers, data scientists, and clinicians to address challenges in this interdisciplinary field. All submissions of original research, reviews, and perspectives in topics related to recent and crucial insights into using AI approaches in optical biosensors for healthcare applications are encouraged and welcome, including, but not limited, to AI-based sensing theory, smart biosensor systems, wearable optical devices, and AI models, as well as their healthcare applications.

Dr. Shuo Chen
Prof. Dr. Min Guo
Dr. Renzhe Bi
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 biosensors
  • healthcare
  • machine learning
  • deep learning
  • Internet of Things
  • point-of-care testing
  • diagnosis and prognosis

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

17 pages, 3435 KB  
Article
Machine Learning-Assisted Rapid Optical Imaging for Label-Free CAR T-Cell Detection in Whole Blood
by Nanxi Yu, Ryan M. Porter, Xinyu Zhou, Wenwen Jing, Fenni Zhang, Eider F. Moreno Cortes, Paula A. Lengerke Diaz, Jose V. Forero Forero, Erica Forzani, Januario E. Castro and Shaopeng Wang
Biosensors 2026, 16(5), 240; https://doi.org/10.3390/bios16050240 - 24 Apr 2026
Viewed by 763
Abstract
Chimeric antigen receptor (CAR) T-cell therapy is an effective treatment for hematologic malignancies. However, it is limited by high costs, risk of severe toxicities such as cytokine release syndrome and neurotoxicity, and heterogeneous patient responses. The current therapy monitoring depends largely on subjective [...] Read more.
Chimeric antigen receptor (CAR) T-cell therapy is an effective treatment for hematologic malignancies. However, it is limited by high costs, risk of severe toxicities such as cytokine release syndrome and neurotoxicity, and heterogeneous patient responses. The current therapy monitoring depends largely on subjective symptom assessment, routine laboratory tests, and basic vital signs, without real-time, quantitative evaluation of CAR T-cell expansion or activation in clinical practice. This lack of timely immune monitoring hampers individualized care and contributes to increased treatment costs. To address this need, we present a proof-of-concept, label-free rapid optical imaging (ROI) biosensor with automated machine learning analysis for direct quantification of CAR T-cells from whole blood. This microfluidic platform integrates red blood cell (RBC) removal, CAR T-cell capture, and imaging-based quantification on a single chip, eliminating the need for centrifugation, staining, and operator-dependent interpretation. For validation, 50 μL whole blood samples spiked with Jurkat cells expressing CD19 CARs underwent RBC depletion by agglutination and microfiltration. The remaining blood components were then incubated on a sensor chip functionalized with recombinant CD19 protein. Captured CAR T-cells were imaged by brightfield microscopy and automatically enumerated using a machine learning algorithm trained on fluorescence-validated cells. The CD-19 cells’ capture performance was validated by flow cytometry and fluorescence imaging. The trained machine learning model validated at 88% sensitivity and 96% specificity. Buffer and whole blood calibration curves were established across clinically relevant concentrations (1–1000 cells/µL) with triple replicates. The results showed high correlation (0.975 and 0.990 R2) between the spiked concentration and the detected CAR T-cells, with a 95% certainty limit of detection (LOD) and quantification (LOQ) of 0.6 and 1.1 cells/µL for spiked buffer, and 14 and 67 cells/µL for spiked whole-blood, respectively. Full article
(This article belongs to the Special Issue Optical Biosensors for Healthcare: An Artificial Intelligence Approach)
►▼ Show Figures

Figure 1

21 pages, 8090 KB  
Article
Effects of Sample Deposition Medium and Drying on Spectroscopic Quantification of Lipid Biomarkers in Respiratory Distress Syndrome
by Zixing (Hings) Luo, Waseem Ahmed, Anthony D. Postle, Ahilanandan Dushianthan, Michael P. W. Grocott and Ganapathy Senthil Murugan
Biosensors 2026, 16(3), 154; https://doi.org/10.3390/bios16030154 - 10 Mar 2026
Viewed by 621
Abstract
Rapid point of care assessment of pulmonary surfactant composition by measuring the lecithin/sphingomyelin (L/S) ratio could improve management of patients with neonatal respiratory distress syndrome (nRDS). Attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) offers a practical route to making such measurements, but [...] Read more.
Rapid point of care assessment of pulmonary surfactant composition by measuring the lecithin/sphingomyelin (L/S) ratio could improve management of patients with neonatal respiratory distress syndrome (nRDS). Attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) offers a practical route to making such measurements, but the influence of the sample solvent prior to drying on measurement repeatability is poorly understood. We compare films dried from dichloromethane (DCM) and water (AQ) solvents (DCM-dry route vs. AQ-dry route) by ATR-FTIR and show that spectra from the AQ-dry route increased the signal-to-noise ratio (SNR) of a representative (2920 cm−1) absorption peak for the mixture from 20.13 to 128.20 and for human endotracheal aspirate (ETA) from 6.33 to 8.13. A mixed nested analysis of variance (ANOVA) showed that drying route accounted for 89.52% of mixture peak height variance and reduced percent relative standard deviation (%RSD) from 23.5% to 16.2%, corroborated by multivariate analysis for ETA. We further demonstrate that partial least squares regression (PLSR) models trained on AQ-dry mixture spectra predicted L/S (R2 = 0.91; root mean square error (RMSE) = 0.31) with 95% prediction interval grey-zone interpretation around L/S = 2.2, complemented by a receiver operating characteristic area under the curve (ROC-AUC) of 0.978. Full article
(This article belongs to the Special Issue Optical Biosensors for Healthcare: An Artificial Intelligence Approach)
►▼ Show Figures

Figure 1

19 pages, 8504 KB  
Article
Fiber-Based Ultra-High-Speed Diffuse Speckle Contrast Analysis System for Deep Blood Flow Sensing Using a Large SPAD Camera
by Quan Wang, Renzhe Bi, Songhua Zheng, Ahmet T. Erdogan, Yi Qi, Chenxu Li, Yuanyuan Hua, Mingliang Pan, Yining Wang, Neil Finlayson, Malini Olivo, Robert K. Henderson and David Day-Uei Li
Biosensors 2025, 15(8), 514; https://doi.org/10.3390/bios15080514 - 7 Aug 2025
Cited by 4 | Viewed by 2478
Abstract
Diffuse speckle contrast analysis (DSCA), also called speckle contrast optical spectroscopy (SCOS), has emerged as a groundbreaking optical imaging technique for tracking dynamic biological processes, including blood flow and tissue perfusion. Recent advancements in single-photon avalanche diode (SPAD) cameras have unlocked exceptional sensitivity, [...] Read more.
Diffuse speckle contrast analysis (DSCA), also called speckle contrast optical spectroscopy (SCOS), has emerged as a groundbreaking optical imaging technique for tracking dynamic biological processes, including blood flow and tissue perfusion. Recent advancements in single-photon avalanche diode (SPAD) cameras have unlocked exceptional sensitivity, time resolution, and high frame-rate imaging capabilities. Despite this, the application of large-format SPAD arrays in speckle contrast analysis is still relatively uncommon. This study introduces a pioneering use of a large-format SPAD camera for DSCA. By harnessing the camera’s high temporal resolution and photon-detection efficiency, we significantly enhance the accuracy and robustness of speckle contrast measurements. Our experimental results demonstrate the system’s remarkable ability to capture rapid temporal variations over a broad field of view, enabling detailed spatiotemporal analysis. Through simulations, phantom experiments, and in vivo studies, we validated the proposed approach’s potential for cerebral blood flow and functional tissue monitoring. This work highlights the transformative impact of large SPAD cameras on DSCA, setting the stage for breakthroughs in optical imaging. Full article
(This article belongs to the Special Issue Optical Biosensors for Healthcare: An Artificial Intelligence Approach)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop