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Applied Sciences
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Recent Progress and Challenges of Digital Health and Bioengineering
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Recent Progress and Challenges of Digital Health and Bioengineering

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: 20 August 2025 | Viewed by 7712

Special Issue Editors

Prof. Dr. Hariton-Nicolae Costin

E-Mail Website
Guest Editor
Institute of Computer Science of the Romanian Academy, Iasi Branch, 700481 Iasi, Romania
Interests: biosignal processing; biomedical image processing; artificial intelligence (neural networks, fuzzy systems, bio-inspired algorithms); (bio)sensors/transducers; e-health and telemedicine; assistive technologies
Special Issues, Collections and Topics in MDPI journals
Dr. Gladiola Petroiu

E-Mail Website
Guest Editor
Faculty of Medical Bioengineering, Grigore T. Popa University of Medicine and Pharmacy of Iași, 9-13 Kogalniceanu Str., 700454 Iași, Romania
Interests: biomedical signal processing; e-health; assistive technologies; wearable medical sensors and devices; robot process automation
Special Issues, Collections and Topics in MDPI journals
Dr. Cristian Rotariu

E-Mail Website
Guest Editor
Faculty of Medical Bioengineering, Grigore T. Popa University of Medicine and Pharmacy of Iași, 9-13 Kogalniceanu Str., 700454 Iași, Romania
Interests: biomedical signal and medical image processing; telemedicine; assistive technologies; wearable medical sensors and devices
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is connected to the 12th IEEE International Conference on e-Health and Bioengineering (EHB 2024 http://www.ehbconference.ro/Home.aspx). We welcome extended versions of submissions from the conference and regular submissions within this field. Also, we expect papers to be presented at the EHB 2023 conference.

The main objective of the EHB 2023 and 2024 conferences and of this Special Issue is to cover a broad spectrum of up-to-date topics of Digital Health and Medical Bioengineering/Biomedical Engineering by allowing scientists from diverse fields to participate in the presentation, discussion, and evaluation of the latest advances, research challenges, and opportunities in hardware/software technologies, medical devices/instrumentation, biosignal and image processing, biomaterials, biomechanics, biotechnologies, bioinformatics, micro and nanotechnologies, systems biology or virtual physiological human. Nevertheless, we also welcome regular research or review articles on all aspects of digital health and bioengineering.

The topics include but are not limited to:

  • Medical robotics and actuators;
  • Medical imaging, image processing, and analysis;
  • Biosignal processing;
  • Telemedicine, e-health, and telecommunications;
  • Wearable systems and sensors, m-health and p-health systems;
  • Internet in healthcare and medical web portals;
  • Cloud computing;
  • Decision support systems and artificial intelligence in medicine;
  • Internet and network applications;
  • Wireless sensor networks;
  • Laser technology and optical communication;
  • Microelectronics;
  • Healthcare in the space environment;
  • Optoelectronics for health;
  • Embedded systems;
  • Biomechanics;
  • Micro and nanotechnology for medicine;
  • Medical physics and biophysics;
  • Medical devices and equipment;
  • Measurement and instrumentation in bioengineering;
  • Biometrics, forensics, and security;
  • Health technology assessment;
  • Rehabilitative and assistive technologies;
  • Electromagnetic compatibility;
  • Instrumental analysis and laboratory technologies;
  • Molecular bioengineering;
  • Bioengineering in dental and oral health;
  • Multimedia applications for medical and healthcare education and e-learning;
  • Neurosciences;
  • Biomedical sciences communication and career development.

Prof. Dr. Hariton-Nicolae Costin
Dr. Gladiola Petroiu
Dr. Cristian Rotariu
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

  • medical robotics and actuators
  • medical imaging, image processing, and analysis
  • biosignal processing
  • telemedicine, e-health, and telecommunications
  • wearable systems and sensors, m-health and p-health systems
  • internet in healthcare and medical web portals
  • cloud computing
  • decision support systems and artificial intelligence in medicine
  • internet and network applications
  • wireless sensor networks
  • laser technology and optical communication
  • microelectronics
  • healthcare in the space environment
  • optoelectronics for health
  • embedded systems
  • biomechanics
  • micro and nanotechnology for medicine
  • medical physics and biophysics
  • medical devices and equipment
  • measurement and instrumentation in bioengineering
  • biometrics, forensics, and security
  • health technology assessment
  • rehabilitative and assistive technologies
  • electromagnetic compatibility
  • instrumental analysis and laboratory technologies
  • molecular bioengineering
  • bioengineering in dental and oral health
  • multimedia applications for medical and healthcare education and e-learning
  • neurosciences
  • biomedical sciences communication and career development

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.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (4 papers)

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Research

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14 pages, 1269 KiB  
Article
Cognitive Electronic Unit for AI-Guided Real-Time Echocardiographic Imaging
by Emanuele De Luca, Emanuele Amato, Vincenzo Valente, Marianna La Rocca, Tommaso Maggipinto, Roberto Bellotti and Francesco Dell’Olio
Appl. Sci. 2025, 15(9), 5001; https://doi.org/10.3390/app15095001 (registering DOI) - 30 Apr 2025
Abstract
Echocardiography is a fundamental tool in cardiovascular diagnostics, providing radiation-free real-time assessments of cardiac function. However, its accuracy strongly depends on operator expertise, resulting in inter-operator variability that affects diagnostic consistency. Recent advances in artificial intelligence have enabled new applications for real-time image [...] Read more.
Echocardiography is a fundamental tool in cardiovascular diagnostics, providing radiation-free real-time assessments of cardiac function. However, its accuracy strongly depends on operator expertise, resulting in inter-operator variability that affects diagnostic consistency. Recent advances in artificial intelligence have enabled new applications for real-time image classification and probe guidance, but these typically rely on large datasets and specialized hardware such as GPU-based or embedded accelerators, limiting their clinical adoption. Here, we address this challenge by developing a cognitive electronic unit that integrates convolutional neural network (CNN) models and an inertial sensor for assisted echocardiography. We show that our system—powered by an NVIDIA Jetson Orin Nano—can effectively classify standard cardiac views and differentiate good-quality from poor-quality ultrasound images in real time even when trained on relatively small datasets. Preliminary results indicate that the combined use of CNN-based classification and inertial sensor-based feedback can reduce inter-operator variability and may also enhance diagnostic precision. By lowering barriers to data acquisition and providing real-time guidance, this system has the potential to benefit both novice and experienced sonographers, helping to standardize echocardiographic exams and improve patient outcomes. Further data collection and model refinements are ongoing, progressing the way for a more robust and widely applicable clinical solution. Full article
(This article belongs to the Special Issue Recent Progress and Challenges of Digital Health and Bioengineering)
14 pages, 789 KiB  
Article
Machine Learning-Based Model for Emergency Department Disposition at a Public Hospital
by Savaş Sezik, Mustafa Özgür Cingiz and Esma İbiş
Appl. Sci. 2025, 15(3), 1628; https://doi.org/10.3390/app15031628 - 6 Feb 2025
Viewed by 1286
Abstract
With the increasing global demand for artificial intelligence solutions, their role in medicine is also expected to grow as a result of their advantage of easy access to clinical data. Machine learning models, with their ability to process large amounts of data, can [...] Read more.
With the increasing global demand for artificial intelligence solutions, their role in medicine is also expected to grow as a result of their advantage of easy access to clinical data. Machine learning models, with their ability to process large amounts of data, can help solve clinical issues. The aim of this study was to construct seven machine learning models to predict the outcomes of emergency department patients and compare their prediction performance. Data from 75,803 visits to the emergency department of a public hospital between January 2022 to December 2023 were retrospectively collected. The final dataset incorporated 34 predictors, including two sociodemographic factors, 23 laboratory variables, five initial vital signs, and four emergency department-related variables. They were used to predict the outcomes (mortality, referral, discharge, and hospitalization). During the study period, 316 (0.4%) visits ended in mortality, 5285 (7%) in referral, 13,317 (17%) in hospitalization, and 56,885 (75%) in discharge. The disposition accuracy (sensitivity and specificity) was evaluated using 34 variables for seven machine learning tools according to the area under the curve (AUC). The AUC scores were 0.768, 0.694, 0.829, 0.879, 0.892, 0.923, and 0.958 for Adaboost, logistic regression, K-nearest neighbor, LightGBM, CatBoost, XGBoost, and Random Forest (RF) models, respectively. The machine learning models, especially the discrimination ability of the RF model, were much more reliable in predicting the clinical outcomes in the emergency department. XGBoost and CatBoost ranked second and third, respectively, following RF modeling. Full article
(This article belongs to the Special Issue Recent Progress and Challenges of Digital Health and Bioengineering)
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18 pages, 9237 KiB  
Article
Highly Photoresponsive Vertically Stacked Silicon Nanowire Photodetector with Biphasic Current Stimulator IC for Retinal Prostheses
by Taehwan Kim, Seungju Han and Sangmin Lee
Appl. Sci. 2024, 14(19), 8831; https://doi.org/10.3390/app14198831 - 1 Oct 2024
Viewed by 3634
Abstract
This paper presents an integrated approach for a retinal prosthesis that overcomes the scalability challenges and limitations of conventional systems that use external cameras. Silicon nanowires (SiNWs) are utilized as photonic sensors due to their nanoscale dimensions and high surface-to-volume ratio. To enhance [...] Read more.
This paper presents an integrated approach for a retinal prosthesis that overcomes the scalability challenges and limitations of conventional systems that use external cameras. Silicon nanowires (SiNWs) are utilized as photonic sensors due to their nanoscale dimensions and high surface-to-volume ratio. To enhance these properties and achieve high photoresponsivity, our research team developed a vertically stacked SiNW structure using a fabrication method entirely based on dry etching. The fabricated SiNW photodetector demonstrated excellent electrical and optical characteristics, including linear I–V characteristics that confirmed ohmic contact formation and high photoresponsivity exceeding 105 A/W across the 400–800 nm wavelength range. The SiNW photodetector, following its integration with a switched capacitor stimulator circuit, exhibited a proportional increase in stimulation current in response to higher light intensity and increased SiNW density. In vitro experiments confirmed the efficacy of the integrated system in inducing neural responses from retinal cells, as indicated by an increased number of neural spikes observed at higher light intensities and SiNW densities. This study contributes to sensor technology by demonstrating an approach to integrating nanostructures and electronic components, which enhances control and functionality. Full article
(This article belongs to the Special Issue Recent Progress and Challenges of Digital Health and Bioengineering)
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Review

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50 pages, 3331 KiB  
Review
Artificial Intelligence in Ophthalmology: Advantages and Limits
by Hariton-Nicolae Costin, Monica Fira and Liviu Goraș
Appl. Sci. 2025, 15(4), 1913; https://doi.org/10.3390/app15041913 - 12 Feb 2025
Viewed by 1953
Abstract
In recent years, artificial intelligence has begun to play a salient role in various medical fields, including ophthalmology. This extensive review is addressed to ophthalmologists and aims to capture the current landscape and future potential of AI applications for eye health. From automated [...] Read more.
In recent years, artificial intelligence has begun to play a salient role in various medical fields, including ophthalmology. This extensive review is addressed to ophthalmologists and aims to capture the current landscape and future potential of AI applications for eye health. From automated retinal screening processes and machine learning models predicting the progression of ocular conditions to AI-driven decision support systems in clinical settings, this paper provides a comprehensive overview of the clinical implications of AI in ophthalmology. The development of AI has opened new horizons for ophthalmology, offering innovative solutions to improve the accuracy and efficiency of ocular disease diagnosis and management. The importance of this paper lies in its potential to strengthen collaboration between researchers, ophthalmologists, and AI specialists, leading to transformative findings in the early identification and treatment of eye diseases. By combining AI potential with cutting-edge imaging methods, novel biomarkers, and data-driven approaches, ophthalmologists can make more informed decisions and provide personalized treatment for their patients. Furthermore, this paper emphasizes the translation of basic research outcomes into clinical applications. We do hope this comprehensive review will act as a significant resource for ophthalmologists, researchers, data scientists, healthcare professionals, and managers in the healthcare system who are interested in the application of artificial intelligence in eye health. Full article
(This article belongs to the Special Issue Recent Progress and Challenges of Digital Health and Bioengineering)
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