Machine Learning Applications in Biomedical Engineering
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".
Deadline for manuscript submissions: 20 May 2026
Special Issue Editors
Interests: time–frequency analysis; biomedical signal processing; applied statistical signal processing; digital signal processing; IoT
Special Issues, Collections and Topics in MDPI journals
Interests: signal processing and analysis; biomedical signal processing; video surveillance; ANN
Special Issues, Collections and Topics in MDPI journals
Interests: numerical methods in electromagnetism; FEM; BEM; bioheat equations; electromagnetic theory
Special Issues, Collections and Topics in MDPI journals
Interests: navigation safety; machine-based methods in maritime system reliability; fault diagnostics; hydrographic surveying; communication technologies; maritime signalling facilities; marine automation; IoT in the maritime industry
Special Issue Information
Dear Colleagues,
Advancements in computational power have led to the development of machine learning (ML)-based models and techniques. Moreover, ML-based systems and algorithms have had a significant impact on the study of medical phenomena because they can be used to analyze biomedical signals, thereby improving diagnostic accuracy. Innovative applications that utilize linear and logistic regression, decision trees, support vector machines, k-means, KNN, random forests, and Bayesian algorithms, among other methods, require multidisciplinary approaches that combine the expertise of both engineering and medical professionals. The application of biomedical signal processing and analysis not only enhances the reliability of diagnostics, but also deepens our understanding of "human systems." In summary, modern machine learning-based devices contribute to improving quality of life.
Conditions such as muscle or speech impairments, diseases, and other abnormalities can severely affect daily living and have substantial social and economic repercussions. In biomedical contexts, advanced signal processing methods that utilize machine learning techniques can significantly improve the everyday communication and activities of patients with this category of impairment or illness. Some popular biomedical engineering applications include prosthetic limbs, 3D organ printers, and medical imaging technology (e.g., MRI, CT scan, ultrasound).
The main topics of this Special Issue include, but are not limited to, the following:
- Biomedical-based ML models;
- Biomedical instrumentation;
- Development of new biomaterials and tissue engineering;
- Bioelectromagnetism and biomechanics;
- Acquisition, filtering, and processing of biomedical signals;
- ML applications in diagnostics, health monitoring, and medical assistance;
- Practical applications for health monitoring and wearable devices.
Dr. Joško Šoda
Prof. Dr. Igor Vujović
Dr. Hrvoje Dodig
Guest Editors
Dr. Ivana Golub Medvešek
Guest Editor Assistant
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. Applied Sciences 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 2400 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
- machine and deep learning
- biomedical instrumentation
- medical imaging
- biomedical signal processing
- health monitoring
- additive manufacturing in biomedical engineering
- robots in biomedical engineering
- development of new biomaterials
- bioelectromagnetism
- biomechanics
- 3D organ printers and prosthetics
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