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Recent Advances in Digital Healthcare and Applications

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

Deadline for manuscript submissions: closed (20 March 2023) | Viewed by 8217

Special Issue Editors


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Guest Editor
1. University Research and Innovation Center (EKIK), Obuda University, Budapest, Hungary
2. School of Computing, Queen’s University in Kingston, Kingston, ON K7L 3N6, UK
Interests: medical cyber-physical systems; surgical robotics; telerobotics; time-delayed systems; image-guided surgery; surgical data science; digital infection prevention & control; autonomous vehicle safety; agrifood robotics; Internet of Medical Things; technology transfer and innovation management
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Guest Editor
Physiological Controls Research Center, Óbuda University, 1034 Budapest, Hungary
Interests: biomedical and control systems; machine learning-based applications

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Guest Editor
Physiological Controls Research Center, Research, Innovation and Service Center, Òbuda University, 1034 Budapest, Hungary
Interests: physiological modeling and control; modern robust control theory; cyber-medical systems; biomedical engineering

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Guest Editor
Health Economics Research Center, University Research and Innovation Center, Óbuda University, Bécsi út 96/B, H-1034 Budapest, Hungary
Interests: health economics; digital health

Special Issue Information

Dear Colleagues,

The research objects of the cyber-medical domain are highly complex due to the interdisciplinary nature coming from the need to combine economical, technical and medical/physiological knowledge. Achieving development in this field is extremely research-intensive and requires complex thinking and the involvement of regulatory requirements from the beginning of the conceptualization which demand evidence-based certification guidelines.

Modern medical devices are based on deep understandings of complex phenomena related to physiology, biology, pathology, etc., connected to the economical knowledge determining worthiness and eligibility of funding in accordance with regulations and guidelines. The most challenging task here is that even the base research should serve the latter marketability in order to improve the life of people through improved healthcare services.

The operations of healthcare institutions and healthcare services are extremely complex. They have evolved enormously over the past decade, and digitalization has been especially facilitated by the recent pandemic. In the case of medical devices, innovative data-integration solutions, such as augmented and virtual reality devices, wearable devices, and diagnostic solutions, represent a new segment of the market, and their development is a priority area. Nevertheless, these anticipated technological developments have the potential to increase inequalities and open discussion around responsible and ethically aligned design. The challenges posed by new technologies and methodologies can easily affect the end-users of the technologies in the incomplete legal-regulatory environment and the "sea" of healthcare systems and solutions. While it is driven by a rising, fast-acting start-up culture, the speed of development and deployment carries risks, especially in the healthcare domain. The new EU MDR/IVDR regulation has been a milestone in the legal regulatory environment but has made it more difficult to enter the market . Novel standardization efforts appeared to facilitate the development process for medical devices. The new procedures require a complex approach, beginning in the design phase, to comply with the regulatory environment, from the need to conduct evidence-based research to ensuring the system's life cycle. AI and life-model-based solutions are a particular challenge, as the certification of healthcare products requires the interpretation and explanation of the decision-making process of these devices and systems.

The aim of this Special Issue is to provide a space to showcase the challenges encountered at all stages of the complex development life cycle in digital health and medical technology through exciting and innovative pilot works. Properly conducted systematic literature reviews, well-described and sustained scientific concepts, pilot technology and algorithm designs, successfully applied protocol design, and development-related scientific articles are welcomed. It is only through these good and best practices that it will possible to establish the right methods of evidence-based digital health.

This Special Issue will select from the best research papers on a global scale to introduce novel achievements, techniques, and findings related to the digital healthcare domain. We welcome original research and review articles related to the topics provided.

Potential topics to be covered:

  • Development of complex cyber-medical systems;
  • Worthiness and marketability regarding medical systems;
  • Data-driven modeling and simulation of biological systems;
  • Management and performance assessment related to cyber-medical systems;
  • Evidence based research of cyber-medical systems;
  • Machine learning and artificial intelligence techniques in the digital healthcare domain;
  • Control and estimation of biological and physiological systems supporting digital healthcare solutions;
  • Research of distributed, parallel, and ledger technologies in the cyber-medical domain.

Dr. Tamás Haidegger
Dr. Eigner György
Prof. Dr. Levente Kovács
Dr. Zrubka Zsombor
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. 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.

Published Papers (3 papers)

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Research

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23 pages, 1630 KiB  
Article
Detection of Physical Activity Using Machine Learning Methods Based on Continuous Blood Glucose Monitoring and Heart Rate Signals
by Lehel Dénes-Fazakas, Máté Siket, László Szilágyi, Levente Kovács and György Eigner
Sensors 2022, 22(21), 8568; https://doi.org/10.3390/s22218568 - 7 Nov 2022
Cited by 2 | Viewed by 2389
Abstract
Non-coordinated physical activity may lead to hypoglycemia, which is a dangerous condition for diabetic people. Decision support systems related to type 1 diabetes mellitus (T1DM) still lack the capability of automated therapy modification by recognizing and categorizing the physical activity. Further, this desired [...] Read more.
Non-coordinated physical activity may lead to hypoglycemia, which is a dangerous condition for diabetic people. Decision support systems related to type 1 diabetes mellitus (T1DM) still lack the capability of automated therapy modification by recognizing and categorizing the physical activity. Further, this desired adaptive therapy should be achieved without increasing the administrative load, which is already high for the diabetic community. These requirements can be satisfied by using artificial intelligence-based solutions, signals collected by wearable devices, and relying on the already available data sources, such as continuous glucose monitoring systems. In this work, we focus on the detection of physical activity by using a continuous glucose monitoring system and a wearable sensor providing the heart rate—the latter is accessible even in the cheapest wearables. Our results show that the detection of physical activity is possible based on these data sources, even if only low-complexity artificial intelligence models are deployed. In general, our models achieved approximately 90% accuracy in the detection of physical activity. Full article
(This article belongs to the Special Issue Recent Advances in Digital Healthcare and Applications)
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17 pages, 5170 KiB  
Article
Calibration-Aimed Comparison of Image-Cytometry- and Flow-Cytometry-Based Approaches of Ploidy Analysis
by Viktor Zoltán Jónás, Róbert Paulik, Miklós Kozlovszky and Béla Molnár
Sensors 2022, 22(18), 6952; https://doi.org/10.3390/s22186952 - 14 Sep 2022
Cited by 1 | Viewed by 1448
Abstract
Ploidy analysis is the fundamental method of measuring DNA content. For decades, the principal way of conducting ploidy analysis was through flow cytometry. A flow cytometer is a specialized tool for analyzing cells in a solution. This is convenient in laboratory environments, but [...] Read more.
Ploidy analysis is the fundamental method of measuring DNA content. For decades, the principal way of conducting ploidy analysis was through flow cytometry. A flow cytometer is a specialized tool for analyzing cells in a solution. This is convenient in laboratory environments, but prohibits measurement reproducibility and the complete detachment of sample preparation from data acquisition and analysis, which seems to have become paramount with the constant decrease in the number of pathologists per capita all over the globe. As more open computer-aided systems emerge in medicine, the demand for overcoming these shortcomings, and opening access to even more (and more flexible) options, has also emerged. Image-based analysis systems can provide an alternative to these types of workloads, placing the abovementioned problems in a different light. Flow cytometry data can be used as a reference for calibrating an image-based system. This article aims to show an approach to constructing an image-based solution for ploidy analysis, take measurements for a basic comparison of the data produced by the two methods, and produce a workflow with the ultimate goal of calibrating the image-based system. Full article
(This article belongs to the Special Issue Recent Advances in Digital Healthcare and Applications)
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Other

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28 pages, 8639 KiB  
Systematic Review
Efficacy of Robot-Assisted Gait Therapy Compared to Conventional Therapy or Treadmill Training in Children with Cerebral Palsy: A Systematic Review with Meta-Analysis
by Irene Cortés-Pérez, Noelia González-González, Ana Belén Peinado-Rubia, Francisco Antonio Nieto-Escamez, Esteban Obrero-Gaitán and Héctor García-López
Sensors 2022, 22(24), 9910; https://doi.org/10.3390/s22249910 - 16 Dec 2022
Cited by 4 | Viewed by 3162
Abstract
Background: Motor, gait and balance disorders reduce functional capabilities for activities of daily living in children with cerebral palsy (CP). Robot-assisted gait therapy (RAGT) is being used to complement conventional therapy (CT) or treadmill therapy (TT) in CP rehabilitation. The aim of this [...] Read more.
Background: Motor, gait and balance disorders reduce functional capabilities for activities of daily living in children with cerebral palsy (CP). Robot-assisted gait therapy (RAGT) is being used to complement conventional therapy (CT) or treadmill therapy (TT) in CP rehabilitation. The aim of this systematic review is to assess the effect of RAGT on gait, balance and functional independence in CP children, in comparison to CT or TT. Methods: We have conducted a systematic review with meta-analysis. A search in PubMed Medline, Web of Science, Scopus, CINAHL, PEDro and SciELO has been conducted for articles published until October 2022. Controlled clinical trials (CCT), in which RAGT was compared to TT or CT and assessed gait speed, step and stride length, width step, walking distance, cadence, standing ability, walking, running and jumping ability, gross motor function and functional independence in children with CP, have been included. Methodological quality was assessed with the PEDro scale and the pooled effect was calculated with Cohen’s Standardized Mean Difference (SMD) and its 95% Confidence Interval (95% CI). Results: A total of 15 CCTs have been included, providing data from 413 participants, with an averaged methodological quality of 5.73 ± 1.1 points in PEDro. The main findings of this review are that RAGT shows better results than CT in the post-intervention assessment for gait speed (SMD 0.56; 95% CI 0.03 to 1.1), walking distance (SMD 2; 95% CI 0.36 to 3.65) and walking, running and jumping ability (SMD 0.63; 95% CI 0.12 to 1.14). Conclusions: This study shows that the effect of RAGT is superior to CT on gait speed, walking distance and walking, running and jumping ability in post-intervention, although no differences were found between RAGT and TT or CT for the remaining variables. Full article
(This article belongs to the Special Issue Recent Advances in Digital Healthcare and Applications)
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