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Applied Sciences
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Machine Learning in Biomedical Applications
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Machine Learning in Biomedical Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: 30 August 2024 | Viewed by 1862

Special Issue Editors

Dr. Zunyi Tang

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Guest Editor
Center for Medical Education and Career Development, Fukushima Medical University, Fukushima 960-1295, Japan
Interests: biomedical signal processing; biomedical instrumentation; health informatics; artificial intelligence
Dr. Yoshinobu Murayama

E-Mail Website
Guest Editor
Bio-physiological Engineering Laboratory (BPELAB), Department of Electrical and Electronics Engineering, College of Engineering, Nihon University, Koriyama, Japan
Interests: basal body temperature; healthcare data analysis; assisted reproductive technology; embryo engineering
Prof. Dr. Mitsuhiro Ogawa

E-Mail Website
Guest Editor
Department of Information and Electronic Engineering, Teikyo University, Tokyo, Japan
Interests: biomedical engineering; welfare engineering; game science
Prof. Dr. Shuxue Ding

E-Mail Website
Guest Editor
School of Artificial Intelligence, Guilin University of Electronic Technology, Guilin, China
Interests: signal and image processing; artificial intelligence; big data

Special Issue Information

Dear Colleagues,

Machine learning (ML) has emerged as a transformative force in the field of biomedical research, presenting unprecedented opportunities to revolutionize preventive measures, diagnostics, treatment strategies, and healthcare outcomes. This Special Issue focuses on the diverse applications of machine learning in the biomedical domain, highlighting its potential to extract meaningful insights from complex biomedical data and biological information.

Contributions to this Special Issue explore a broad spectrum of topics, including, but not limited to, intelligent physiological monitoring and sensing using wearable sensors and smart devices, machine learning and artificial intelligence methodologies for biomedical signal/data measurement analysis and interpretation, intelligent decision support systems for enhancing health outcomes, intelligent informatics for extended digital health reality, and data science and data engineering for biomedicine and health, as well as personalized and pervasive health technologies.

Researchers and practitioners are invited to submit their original work, addressing the challenges and breakthroughs in applying machine learning techniques to biomedical problems. Through this Special Issue, we aim to encourage collaboration and knowledge exchange, driving advancements that contribute to the improvement of healthcare outcomes and the overall well-being of individuals.

In this Special Issue, we welcome original research articles and reviews and eagerly anticipate receiving your valuable contributions.

Dr. Zunyi Tang
Dr. Yoshinobu Murayama
Prof. Dr. Mitsuhiro Ogawa
Prof. Dr. Shuxue Ding
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 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • artificial intelligence
  • machine learning
  • physiological measurement and instrumentation
  • biomedical signal and image processing
  • biomedical modeling and computing
  • disease diagnosis and clinical applications
  • wearable sensors and smart devices
  • assisted reproductive technology
  • health informatics
  • home healthcare and wellness management

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

Published Papers (2 papers)

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Research

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19 pages, 6606 KiB  
Article
Efficient Sleep–Wake Cycle Staging via Phase–Amplitude Coupling Pattern Classification
by Vinícius Rosa Cota, Simone Del Corso, Gianluca Federici, Gabriele Arnulfo and Michela Chiappalone
Appl. Sci. 2024, 14(13), 5816; https://doi.org/10.3390/app14135816 - 3 Jul 2024
Viewed by 820
Abstract
The objective and automatic detection of the sleep–wake cycle (SWC) stages is essential for the investigation of its physiology and dysfunction. Here, we propose a machine learning model for the classification of SWC stages based on the measurement of synchronization between neural oscillations [...] Read more.
The objective and automatic detection of the sleep–wake cycle (SWC) stages is essential for the investigation of its physiology and dysfunction. Here, we propose a machine learning model for the classification of SWC stages based on the measurement of synchronization between neural oscillations of different frequencies. Publicly available electrophysiological recordings of mice were analyzed for the computation of phase–amplitude couplings, which were then supplied to a multilayer perceptron (MLP). Firstly, we assessed the performance of several architectures, varying among different input choices and numbers of neurons in the hidden layer. The top performing architecture was then tested using distinct extrapolation strategies that would simulate applications in a real lab setting. Although all the different choices of input data displayed high AUC values (>0.85) for all the stages, the ones using larger input datasets performed significantly better. The top performing architecture displayed high AUC values (>0.95) for all the extrapolation strategies, even in the worst-case scenario in which the training with a single day and single animal was used to classify the rest of the data. Overall, the results using multiple performance metrics indicate that the usage of a basic MLP fed with highly descriptive features such as neural synchronization is enough to efficiently classify SWC stages. Full article
(This article belongs to the Special Issue Machine Learning in Biomedical Applications)
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Review

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27 pages, 6061 KiB  
Review
Artificial Intelligence in Biomaterials: A Comprehensive Review
by Yasemin Gokcekuyu, Fatih Ekinci, Mehmet Serdar Guzel, Koray Acici, Sahin Aydin and Tunc Asuroglu
Appl. Sci. 2024, 14(15), 6590; https://doi.org/10.3390/app14156590 - 28 Jul 2024
Viewed by 659
Abstract
The importance of biomaterials lies in their fundamental roles in medical applications such as tissue engineering, drug delivery, implantable devices, and radiological phantoms, with their interactions with biological systems being critically important. In recent years, advancements in deep learning (DL), artificial intelligence (AI), [...] Read more.
The importance of biomaterials lies in their fundamental roles in medical applications such as tissue engineering, drug delivery, implantable devices, and radiological phantoms, with their interactions with biological systems being critically important. In recent years, advancements in deep learning (DL), artificial intelligence (AI), machine learning (ML), supervised learning (SL), unsupervised learning (UL), and reinforcement learning (RL) have significantly transformed the field of biomaterials. These technologies have introduced new possibilities for the design, optimization, and predictive modeling of biomaterials. This review explores the applications of DL and AI in biomaterial development, emphasizing their roles in optimizing material properties, advancing innovative design processes, and accurately predicting material behaviors. We examine the integration of DL in enhancing the performance and functional attributes of biomaterials, explore AI-driven methodologies for the creation of novel biomaterials, and assess the capabilities of ML in predicting biomaterial responses to various environmental stimuli. Our aim is to elucidate the pivotal contributions of DL, AI, and ML to biomaterials science and their potential to drive the innovation and development of superior biomaterials. It is suggested that future research should further deepen these technologies’ contributions to biomaterials science and explore new application areas. Full article
(This article belongs to the Special Issue Machine Learning in Biomedical Applications)
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