AI/ML-Enabled Biosensing: Shaping the Future of Disease Detection

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: 28 February 2027 | Viewed by 723

Special Issue Editor


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Guest Editor
1. Centro De Investigaciones Biomédicas (CINBIO), Universidade de Vigo, 36310 Vigo, Spain
2. Department of Biotechnology, School of Engineering and Applied Sciences, Bennett University, Greater Noida, UP, India
Interests: microfluidic bio-sensing; point-of-care diagnostics; microfluidic devices; lab-on-chip and molecular biosensing
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Special Issue Information

Dear Colleagues,

This Special Issue, “AI/ML-Enabled Biosensing: Shaping the Future of Disease Detection,” critically examines the convergence of biosensor technologies with artificial intelligence (AI) and machine learning (ML) to revolutionize the field of diagnostic science. Traditional diagnostic approaches often face limitations in sensitivity, speed, and accessibility. Emerging digital biosensors—ranging from electrochemical and optical platforms to wearable and implantable devices—offer real-time, high-throughput detection of clinically relevant biomarkers from minimally invasive samples.

When coupled with AI/ML algorithms, these systems enable advanced signal processing, noise reduction, pattern recognition, and predictive analytics, dramatically enhancing diagnostic accuracy and decision support. This Special Issue highlights interdisciplinary innovations across material science, microfluidics, data science, and clinical medicine that are driving this field forward.

Key topics include AI-integrated point-of-care devices, adaptive biosensor platforms, cloud-based diagnostics, and data privacy and security in digital health. Contributions critically address both the transformative potential and the current challenges, such as standardization, validation, and the interpretability of ML models, as well as regulatory hurdles.

By bringing together research at the interface of biosensing technology and computational intelligence, this issue aims to map a strategic path for deploying smart, accessible, and personalized diagnostic tools that will redefine early disease detection and management in the coming decade.

The scope includes, but is not limited to, the following areas:

  1. AI/ML-Integrated Biosensing Systems
    – Development of biosensors enhanced by machine learning for signal processing, classification, and predictive diagnostics.
  2. Smart and Wearable Biosensors
    – Design and application of wearable, implantable, and point-of-care biosensors for real-time, continuous health monitoring.
  3. Microfluidics and Lab-on-a-Chip Devices
    – Integration of biosensors with microfluidic platforms for multiplexed, high-sensitivity disease detection in miniaturized formats.
  4. Computational Modeling and Diagnostic Algorithms
    – Application of deep learning, neural networks, and federated learning to analyze biosensor data and generate diagnostic insights.
  5. Clinical Applications and Case Studies
    – Use of digital biosensing and AI tools in detecting infectious diseases, cancer, neurological disorders, metabolic diseases, and more.

Dr. Krishna Kant
Guest Editor

Manuscript Submission Information

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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.

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Keywords

  • microfluidic sensing
  • electrochemical bio-sensing
  • point-of-care diagnostics
  • precision diagnostics
  • plasmonic sensing
  • microfluidic devices
  • lab-on-chip

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Published Papers (1 paper)

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Research

17 pages, 3782 KB  
Article
A General Analytic Approach for Rapid Diagnostics by a Simple Algorithm for Fluorescence Single Molecule Counting
by Juiena Hasan and Sangho Bok
Biosensors 2026, 16(5), 270; https://doi.org/10.3390/bios16050270 - 8 May 2026
Viewed by 301
Abstract
Accurate biomolecule quantification at ultralow concentrations remains a major challenge because conventional ensemble assays report population averaged signals and therefore lose sensitivity in low-abundance regimes. Single molecule fluorescence counting can overcome this limitation by converting emission into discrete digital events, but practical implementation [...] Read more.
Accurate biomolecule quantification at ultralow concentrations remains a major challenge because conventional ensemble assays report population averaged signals and therefore lose sensitivity in low-abundance regimes. Single molecule fluorescence counting can overcome this limitation by converting emission into discrete digital events, but practical implementation is often hindered by manual inspection, limited reproducibility, and the complexity of machine learning based analysis. Here, we present a simple and general analytical framework for rapid single molecule detection based on a deterministic threshold algorithm that exploits the temporal signature of fluorescence blinking. The method operates directly on time resolved fluorescence image stacks, applies median filter-based noise suppression, and identifies candidate single molecule events from consecutive frame-to-frame intensity transitions without the need for training data or model fitting. Applied to Alexa Fluor 488, Alexa Fluor 647, and Rhodamine Red–X datasets, the approach reproduced the concentration dependent trends observed by manual counting, while providing more standardized detection under weak signal and high background conditions. Dye specific operating thresholds yielded robust counting behavior and preserved approximately linear concentration dependent response across the tested range. Compared with manual analysis, which required inspection of only selected grid regions, the automated workflow processed full movie stacks and reduced analysis time from ~3 h to ~20 min per concentration, corresponding to an approximately 9-fold gain in efficiency. These results establish an interpretable, computationally lightweight, and experimentally adaptable strategy for fluorescence single molecule counting that can support rapid diagnostics and provide a practical foundation for future extensions in automated localization, clustering, and real time molecular analysis. Full article
(This article belongs to the Special Issue AI/ML-Enabled Biosensing: Shaping the Future of Disease Detection)
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