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Digital Technologies in Sports Medicine and Human Health
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Digital Technologies in Sports Medicine and Human Health

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

Deadline for manuscript submissions: closed (20 February 2025) | Viewed by 13217

Special Issue Editor

Prof. Dr. Givago Souza

E-Mail Website
Guest Editor
Tropical Medicine Institute, Federal University of Pará, Belém 66050-160, Brazil
Interests: balance control; sensory system; inertial sensors; movement analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, a series of studies have shown how digital tools, such as smartphones and smartwatches, equipped with embedded sensors, can be used to assess and monitor people's health, including sensory and motor systems. Wearable devices also play an important role in human health management.

In this Special Issue, we invite the submission of articles on sensory, motor assessment, and disease analysis using smart device technologies (smartphones, smartwatches, wearable devices, or similar devices). These studies may include method descriptions, applications in human diseases and health, or discussions of their potentialities and limitations.

Prof. Dr. Givago Souza
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 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

  • digital health
  • sensory systems
  • motor systems
  • digital biomarkers
  • smart devices
  • wearable devices

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Published Papers (2 papers)

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Research

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15 pages, 3022 KB  
Article
Enhancing Periodontal Bone Loss Diagnosis Through Advanced AI Techniques
by Nader Nabil Fouad Rezallah, George Sherif, Ahmed Z. Abdelkarim and Shereen Afifi
Appl. Sci. 2025, 15(12), 6832; https://doi.org/10.3390/app15126832 - 17 Jun 2025
Viewed by 833
Abstract
Periodontitis is a severe infection that damages the bone surrounding teeth, making severity assessment challenging for dentists. Utilizing artificial intelligence (AI) techniques enhances diagnostic accuracy in radiographic analysis, enabling a more efficient and precise diagnosis. The aim of this research is to develop [...] Read more.
Periodontitis is a severe infection that damages the bone surrounding teeth, making severity assessment challenging for dentists. Utilizing artificial intelligence (AI) techniques enhances diagnostic accuracy in radiographic analysis, enabling a more efficient and precise diagnosis. The aim of this research is to develop an AI-based diagnostic system comprising two deep learning stages, designed to efficiently detect and classify periodontitis from panoramic radiographs. In the proposed two-stage system, a binary classifier is first utilized to determine the presence of periodontal bone loss, which is then localized and classified by the second AI model. Based on our extensive research, we concluded that convolutional neural networks (CNNs) are the most effective type of neural network for addressing our problem. To ensure the accuracy and reliability of results, we developed two robust CNN models, YOLOv8 and MobileNet-v2, that have shown significant performance in similar applications. The primary model, MobileNet-v2, was utilized to detect periodontitis in panoramic radiographs, while the secondary model, YOLOv8, was developed to localize the affected regions and classify the severity level of the periodontitis. A custom dataset was created to comprise 817 panoramic images. Our developed YOLOv8 model achieved a testing precision of 0.74 and a recall of 0.7, while the MobileNet-v2 model achieved a testing accuracy of 0.88 and a recall of 1.0. These results were further validated by an oral radiology specialist to ensure their accuracy and reliability. Our proposed system is considered to be efficient, cost-effective, and easy to use compared to existing systems in the literature and similar software services available in the market for enhancing diagnosis of periodontitis in clinical practice. Full article
(This article belongs to the Special Issue Digital Technologies in Sports Medicine and Human Health)
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Review

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14 pages, 736 KB  
Review
Generative AI in Improving Personalized Patient Care Plans: Opportunities and Barriers Towards Its Wider Adoption
by Mirza Mansoor Baig, Chris Hobson, Hamid GholamHosseini, Ehsan Ullah and Shereen Afifi
Appl. Sci. 2024, 14(23), 10899; https://doi.org/10.3390/app142310899 - 25 Nov 2024
Cited by 6 | Viewed by 11851
Abstract
The main aim of this study is to investigate the opportunities, challenges, and barriers in implementing generative artificial intelligence (Gen AI) in personalized patient care plans (PPCPs). This systematic review paper provides a comprehensive analysis of the current state, potential applications, and opportunities [...] Read more.
The main aim of this study is to investigate the opportunities, challenges, and barriers in implementing generative artificial intelligence (Gen AI) in personalized patient care plans (PPCPs). This systematic review paper provides a comprehensive analysis of the current state, potential applications, and opportunities of Gen AI in patient care settings. This review aims to serve as a key resource for various stakeholders such as researchers, medical professionals, and data governance. We adopted the PRISMA review methodology and screened a total of 247 articles. After considering the eligibility and selection criteria, we selected 13 articles published between 2021 and 2024 (inclusive). The selection criteria were based on the inclusion of studies that report on the opportunities and challenges in improving PPCPs using Gen AI. We found that a holistic approach is required involving strategy, communications, integrations, and collaboration between AI developers, healthcare professionals, regulatory bodies, and patients. Developing frameworks that prioritize ethical considerations, patient privacy, and model transparency is crucial for the responsible deployment of Gen AI in healthcare. Balancing these opportunities and challenges requires collaboration between wider stakeholders to create a robust framework that maximizes the benefits of Gen AI in healthcare while addressing the key challenges and barriers such as explainability of the models, validation, regulation, and privacy integration with the existing clinical workflows. Full article
(This article belongs to the Special Issue Digital Technologies in Sports Medicine and Human Health)
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