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Sensors
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AI and Neural Networks for Advanced Biomedical Sensor Applications
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AI and Neural Networks for Advanced Biomedical Sensor Applications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: closed (31 August 2025) | Viewed by 4233

Special Issue Editor

Prof. Dr. Daniel Graupe

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, IL, USA
Interests: neural engineering; neural networks

Special Issue Information

Dear Colleagues,

Coverage: Original and review papers, including clinical or research reports of preliminary results in:

  • surface-electromyographic (sEMG) signals;
  • electrocardiographic (EKG) signals;
  • electroencephalographic (EEG) signals;
  • magnetocardiographic (MCG) signals;
  • magneto-encephalographic (MEG) signals;
  • quantum dots—in vivo;
  • quantum dots—in vitro;

The papers may cover basics and applications, especially in diagnosis, drug delivery, treatment, control and brain–machine interfaces. Application in cancer research, paraplegia are welcome but may cover any other field of medicine and biology.

The papers my relate (but are not limited) to signal and image processing and spectrum analysis.

Prof. Dr. Daniel Graupe
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 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. Sensors is an international peer-reviewed open access semimonthly 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 2600 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

  • surface-electromyographic (sEMG) signals
  • electrocardiographic (EKG) signals
  • electroencephalographic (EEG) signals
  • magnetocardiographic (MCG) signals
  • magneto-encephalographic (MEG) signals
  • quantum dots—in vivo
  • quantum dots—in vitro

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

Published Papers (3 papers)

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Research

22 pages, 2319 KB  
Article
Enhanced Precision of Fluorescence In Situ Hybridization (FISH) Analysis Using Neural Network-Based Nuclear Segmentation for Digital Microscopy Samples
by Annamaria Csizmadia, Bela Molnar, Marianna Dimitrova Kucarov, Krisztian Koos, Robert Paulik, Dora Kapczar, Laszlo Krenacs, Balazs Csernus, Gergo Papp and Tibor Krenacs
Sensors 2026, 26(3), 873; https://doi.org/10.3390/s26030873 - 28 Jan 2026
Viewed by 1002
Abstract
Introduction: Accurate nuclear segmentation is essential for the reliable diagnostic interpretation of fluorescence in situ hybridization (FISH) results. However, automated 2D digital algorithms often fail in samples with dense or overlapping nuclei, such as lymphomas, due to the loss of spatial depth information. [...] Read more.
Introduction: Accurate nuclear segmentation is essential for the reliable diagnostic interpretation of fluorescence in situ hybridization (FISH) results. However, automated 2D digital algorithms often fail in samples with dense or overlapping nuclei, such as lymphomas, due to the loss of spatial depth information. Here, we tested if AI-based 3D nuclear segmentation can improve the accuracy, reproducibility, and diagnostic reliability of FISH reading in critical situations. Materials and Methods: Formalin-fixed follicular lymphoma sections were FISH-labeled for BCL2 gene rearrangements and digitally scanned in multilayer Z-stacks. The analytic performance in nuclear segmentation of the adaptive thresholding-based FISHQuant, and the freely accessible AI-based NucleAIzer, StarDist, and Cellpose algorithms, were compared to the eye control-based traditional FISH testing, primarily focusing on nuclear segmentation. Results: We revealed that the Cellpose algorithm showed limited sensitivity to low-intensity signals and the adaptive thresholding 2D segmentation, and FISHQuant struggled to resolve densely packed nuclei, occasionally underestimating their counts. In contrast, 3D segmentation across focal planes significantly improved the nuclear separation and signal localization. AI-driven 3D models, especially NucleAIzer and StarDist, showed improved precision, lower variance (VP/VS ≈ 0.96), and improved gene spot correlation (r > 0.82) across multiple focal planes. The similar average number of gene spots per cell nuclei in the AI-based analyses as the eye control counting, despite the elevated number of cell nuclei found with AI, validated the AI nuclear segmentation results. Conclusions: Inaccurate segmentation limits automated diagnostic FISH signal evaluation. Deep learning 3D approaches, particularly NucleAIzer and StarDist, may overcome thresholding and 2D constraints and improve the consistency of nuclear detection, resulting in better classification of pathogenetic gene aberrations with automated workflows in digital pathology. Full article
(This article belongs to the Special Issue AI and Neural Networks for Advanced Biomedical Sensor Applications)
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24 pages, 874 KB  
Article
A Bioinspired Multimodal CNN-LSTM Network for EEG Analysis of Patients in Coma
by Sérgio Baldo-Júnior, Murillo G. Carneiro, João L. M. Barbosa, Liang Zhao, João Batista Destro-Filho, Marcos Campos and Renato Tinós
Sensors 2025, 25(22), 6981; https://doi.org/10.3390/s25226981 - 15 Nov 2025
Viewed by 1676
Abstract
Electroencephalography (EEG) is widely used for diagnosis and evaluation of neurological diseases, despite challenges from its high-dimensional and noisy temporal data, which complicate accurate brain signal classification. This study proposes a multimodal deep learning model combining Convolutional Neural Network (CNN) and Long Short-Term [...] Read more.
Electroencephalography (EEG) is widely used for diagnosis and evaluation of neurological diseases, despite challenges from its high-dimensional and noisy temporal data, which complicate accurate brain signal classification. This study proposes a multimodal deep learning model combining Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) layers to classify EEG signals, integrating patient information as additional modalities. CNN layers effectively extract spatial features and reduce EEG data dimensionality, while LSTM layers capture long-term temporal dependencies. A Genetic Algorithm (GA) selects relevant multimodal features and optimizes CNN-LSTM hyperparameters. The model was applied to outcome classification in comatose patients, achieving improved classifier performance compared to unimodal approaches. Experimental results demonstrate that multimodal integration and GA optimization significantly enhance accuracy, robustness, and generalization. The architecture shows promise for broader EEG classification tasks, potentially advancing clinical decision support based on EEG signals. Full article
(This article belongs to the Special Issue AI and Neural Networks for Advanced Biomedical Sensor Applications)
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24 pages, 5650 KB  
Article
Preliminary Study on Sensor-Based Detection of an Adherent Cell’s Pre-Detachment Moment in a MPWM Microfluidic Extraction System
by Marius-Alexandru Dinca, Mihaita Nicolae Ardeleanu, Dan Constantin Puchianu and Gabriel Predusca
Sensors 2025, 25(9), 2726; https://doi.org/10.3390/s25092726 - 25 Apr 2025
Cited by 1 | Viewed by 1181
Abstract
The extraction of adherent cells, such as B16 murine melanoma cells, from Petri dish cultures is critical in biomedical applications, including cell reprogramming, transplantation, and regenerative medicine. Traditional detachment methods—enzymatic, mechanical, or chemical—often compromise cell viability by altering membrane integrity and disrupting adhesion [...] Read more.
The extraction of adherent cells, such as B16 murine melanoma cells, from Petri dish cultures is critical in biomedical applications, including cell reprogramming, transplantation, and regenerative medicine. Traditional detachment methods—enzymatic, mechanical, or chemical—often compromise cell viability by altering membrane integrity and disrupting adhesion proteins. To address these challenges, this study investigated sensor-based detection of the pre-detachment phase in a MPWM (Microfluidic Pulse Width Modulation) extraction system. Our approach integrates a micromechatronic system with a microfluidic suction circuit, real-time CCD imaging, and computational analysis to detect and characterize the pre-detachment moment before full extraction. A precisely controlled hydrodynamic force field progressively disrupts adhesion in multiple stages, reducing mechanical stress and preserving cell integrity. Real-time video analysis enables continuous monitoring of positional dynamics and oscillatory responses. Image processing and deep learning algorithms determine object center coordinates, allowing the MPWM system to dynamically adjust suction parameters. This optimizes detachment while minimizing liquid absorption and reflux volume, ensuring efficient extraction. By combining microfluidics, sensor detection, and AI-driven image processing, this study established a non-invasive method for optimizing adherent cell detachment. These findings have significant implications for single-cell research, regenerative medicine, and high-throughput biotechnology, ensuring maximal viability and minimal perturbation. Full article
(This article belongs to the Special Issue AI and Neural Networks for Advanced Biomedical Sensor Applications)
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