sensors-logo

Journal Browser

Journal Browser

Advances in Precision Diagnostics for Personalized Healthcare: Imaging, Medical Devices, and Biosensors

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

Deadline for manuscript submissions: 20 March 2027 | Viewed by 447

Editor


E-Mail Website
Guest Editor
CBQF—Centro de Biotecnologia e Química Fina—Laboratório Associado, Escola Superior de Biotecnologia, Universidade Católica Portuguesa, Rua de Diogo Botelho 1327, 4169-005 Porto, Portugal
Interests: digital signal processing and artificial intelligence applied to diagnose neurodegenerative, cardiac, and speech diseases, as well as for the development of biosensors, electronic instrumentation tools, microcontrollers, and smart tools for fast controlling and monitoring of living cities and spaces and agri-food and biological/biomedical systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advances in artificial intelligence (AI) have significantly increased interest in the development of new approaches and solutions that can impact health and medicine, with diagnostic imaging, medical devices, and biosensor systems being a focus of great attention. AI allows the extraction of meaningful patterns from complex medical data such as images, bio-signals, and sensor outputs, enabling improved feature representation and predictive modeling. The growing availability of these types of biomedical data, combined with increased computational power and the rapid evolution of medical devices, is leading to the development of intelligent systems and applications capable of enhancing diagnostic accuracy, automation, and real-time decision-making, thus supporting medical care and enabling more personalized healthcare solutions. Despite this progress, concerns about model transparency, robustness, data privacy, and clinical validation in real-world scenarios remain critical challenges that must be addressed.

This Special Issue aims to present and disseminate original research articles and/or featured reviews that explore new methods, real-world implementations, and interdisciplinary approaches, particularly in bio-signal processing and medical imaging, with emphasis on device innovations, computational algorithms, experimental studies, and emerging applications in biomedicine.

Topics of interest for publication include, but are not limited to, the following:

  • AI in diagnostic imaging;
  • AI for signal processing;
  • Intelligent medical devices;
  • Biosensors and wearables;
  • Real-time monitoring systems;
  • Decision support systems;
  • Explainable AI in healthcare;
  • Signal processing for biomedical data analysis.

Dr. Pedro Miguel Rodrigues
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-anonymized 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

  • diagnostic imaging techniques
  • artificial intelligence
  • applications
  • signal processing
  • medical devices
  • monitoring systems
  • biosensors

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 (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

25 pages, 4773 KB  
Article
Machine Learning Classification of Axillary Lymph Nodes Using Microwave Signals
by Daniela M. Godinho, João M. Felício, Carlos A. Fernandes and Raquel C. Conceição
Sensors 2026, 26(14), 4466; https://doi.org/10.3390/s26144466 - 14 Jul 2026
Viewed by 360
Abstract
Axillary Lymph Nodes (ALNs) can be affected by breast cancer, and the number of affected ALNs is a determinant factor in breast cancer staging. Microwave imaging (MWI) has emerged as a promising technique for ALN assessment, addressing limitations in conventional imaging modalities. This [...] Read more.
Axillary Lymph Nodes (ALNs) can be affected by breast cancer, and the number of affected ALNs is a determinant factor in breast cancer staging. Microwave imaging (MWI) has emerged as a promising technique for ALN assessment, addressing limitations in conventional imaging modalities. This study investigates, for the first time, the classification of ALNs and axillary regions from microwave signals, without image reconstruction. Classification is performed considering realistic morphological characteristics of ALNs reported in the literature and is based solely on geometric differences, which differ from targets previously explored in microwave-based classification studies. Eighty ALN numerical models were mathematically generated based on state-of-the-art anatomical descriptions. Microwave signals were simulated for three scenarios of different complexity, involving one and two ALNs, representing healthy and metastasised conditions. The methodology evaluated multiple combinations of signal types, feature extraction methods, and classifiers, including scenarios with multiple targets, reflecting clinically relevant axillary conditions and limited angular views inherent to axillary imaging. Classification accuracy reached 95% for single-ALN scenarios using kNN, while more complex two-ALN cases achieved accuracies up to 83.3% using SVM. These results demonstrate the potential of microwave signal-based classification to differentiate healthy and metastasised ALNs and axillary regions, supporting future integration with MWI image interpretation. Full article
Show Figures

Figure 1

Back to TopTop