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Applied Sciences
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Intelligent Medicine and Health Care, 2nd Edition
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Intelligent Medicine and Health Care, 2nd Edition

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

Deadline for manuscript submissions: closed (31 May 2025) | Viewed by 8088

Special Issue Editors

Prof. Dr. Chien-Hung Yeh

E-Mail Website
Guest Editor
School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
Interests: electrophysiological signal analysis; intelligent medical treatment; disease health and safety prevention and control; brain science
Special Issues, Collections and Topics in MDPI journals
Dr. Wenbin Shi

E-Mail Website
Guest Editor
School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
Interests: signal processing; brain science; smart health care; artificial intelligence and its application
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xiaojuan Ban

E-Mail Website
Guest Editor
School of Computer & Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
Interests: artificial intelligence; human–brain interface; 3D visualization technique
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Men-Tzung Lo

E-Mail Website
Guest Editor
Department of Biomedical Sciences and Engineering, National Central University, Taoyuan 32001, Taiwan
Interests: bioelectronic devices; signal processing; smart health care; cardiac electrophysiology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Intelligent medicine and health care are flourishing due to the integration of interdisciplinary factors, including artificial intelligence, electrophysiology, signal processing, medical image, complex theory, electronics, and clinics. We are entering an era of in which multidimensional and big data play large roles in developing applications for medicine and health care.

There are many state-of-the-art technologies and new application developments dealing with advanced data analysis and learning, embedded artificial intelligence, clinical decision support, patient ubiquitous monitoring, and rehabilitation aspects.

The Special Issue aims to collect recent research on emerging interdisciplinary methods/techniques/systems for intelligent medicine and health care. Potential topics include, but are not limited, to the following:

  • E-healthcare;
  • Artificial intelligence and machine learning for medicine and health care;
  • Electrophysiological or image processing methods for medicine and health care;
  • Nonlinear dynamics and chaos in health and diseases;
  • Human-centric computer interfaces for health-related environments;
  • Security and privacy models for medical and healthcare systems;
  • Interoperability of heterogeneous network and software technologies for medical and healthcare systems;
  • Smart sensors and wearable devices for medical and healthcare systems;
  • Health data analytics and personalized models in medical and healthcare environments;
  • Neuromodulation and decoding for medical and healthcare systems.

Prof. Dr. Chien-Hung Yeh
Dr. Wenbin Shi
Prof. Dr. Xiaojuan Ban
Prof. Dr. Men-Tzung Lo
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

  • medicine
  • health care
  • wearable devices
  • health data analytics
  • medical signal processing

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

Related Special Issue

Published Papers (5 papers)

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Research

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14 pages, 871 KiB  
Article
Evaluation of Deviations Produced by Soft Tissue Fitting in Virtually Planned Orthognathic Surgery
by Álvaro Pérez-Sala, Pablo Montes Fernández-Micheltorena, Miriam Bobadilla, Ricardo Fernández-Valadés Gámez, Javier Martínez Goñi, Ángela Villanueva, Iñigo Calvo Archanco, José Luis Del Castillo Pardo de Vera, José Luis Cebrián Carretero, Carlos Navarro Cuéllar, Ignacio Navarro Cuellar, Gema Arenas, Ana López López, Ignacio M. Larrayoz and Rafael Peláez
Appl. Sci. 2025, 15(15), 8478; https://doi.org/10.3390/app15158478 - 30 Jul 2025
Viewed by 624
Abstract
Orthognathic surgery (OS) is a complex procedure commonly used to treat dentofacial deformities (DFDs). These conditions, related to jaw position or size and often involving malocclusion, affect approximately 15% of the population. Due to the complexity of OS, accurate planning is essential. Digital [...] Read more.
Orthognathic surgery (OS) is a complex procedure commonly used to treat dentofacial deformities (DFDs). These conditions, related to jaw position or size and often involving malocclusion, affect approximately 15% of the population. Due to the complexity of OS, accurate planning is essential. Digital assessment using computer-aided design (CAD) and computer-aided manufacturing (CAM) tools enhances surgical predictability. However, limitations in soft tissue simulation often require surgeon input to optimize aesthetic results and minimize surgical impact. This study aimed to evaluate the accuracy of virtual surgery planning (VSP) by analyzing the relationship between planning deviations and surgical satisfaction. A single-center, retrospective study was conducted on 16 patients who underwent OS at San Pedro University Hospital of La Rioja. VSP was based on CT scans using Dolphin Imaging software (v12.0, Patterson Dental, St. Paul, MN, USA) and surgeries were guided by VSP-designed occlusal splints. Outcomes were assessed using the Orthognathic Quality of Life (OQOL) questionnaire and deviations were measured through pre- and postoperative imaging. The results showed high satisfaction scores and good overall outcomes, despite moderate deviations from the virtual plan in many cases, particularly among Class II patients. A total of 63% of patients required VSP modifications due to poor soft tissue fitting, with 72% of these being Class II DFDs. Most deviations involved less maxillary advancement than planned, while maintaining optimal occlusion. This suggests that VSP may overestimate advancement needs, especially in Class II cases. No significant differences in satisfaction were observed between patients with low (<2 mm) and high (>2 mm) deviations. These findings support the use of VSP as a valuable planning tool for OS. However, surgeon experience remains essential, especially in managing soft tissue behavior. Improvements in soft tissue prediction are needed to enhance accuracy, particularly for Class II DFDs. Full article
(This article belongs to the Special Issue Intelligent Medicine and Health Care, 2nd Edition)
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22 pages, 3157 KiB  
Article
Data-Driven Forecasting of Acute and Chronic Hepatitis B in Ukraine with Recurrent Neural Networks
by Mykola Butkevych, Sergiy Yakovlev and Dmytro Chumachenko
Appl. Sci. 2025, 15(13), 7573; https://doi.org/10.3390/app15137573 - 6 Jul 2025
Viewed by 613
Abstract
Reliable short-term forecasts of hepatitis B incidence are indispensable for sizing national vaccine and antiviral procurement. However, predictive modelling is complicated when surveillance streams experience reporting delays and episodic under-reporting, as has occurred in Ukraine since 2022. We address this challenge by training [...] Read more.
Reliable short-term forecasts of hepatitis B incidence are indispensable for sizing national vaccine and antiviral procurement. However, predictive modelling is complicated when surveillance streams experience reporting delays and episodic under-reporting, as has occurred in Ukraine since 2022. We address this challenge by training a deliberately compact two-layer long short-term memory (LSTM) network on 72 monthly observations (January 2018–December 2023) drawn from the Public Health Center electronic registry and evaluating performance on a strictly held-out 12-month horizon (January–December 2024). Grid-search optimisation selected a 12-month sliding input window, 64 hidden units per layer, 0.20 dropout, the Adam optimiser, and early stopping. Walk-forward validation showed that the network attained mean squared errors of 411 for acute infection and 76 for chronic infection on the monthly series. When forecasts were aggregated to the cumulative scale, the mean absolute percentage error remained below 1%. This study presents the first peer-reviewed hepatitis B forecasts calibrated on Ukraine’s registry during a period of pronounced reporting instability, demonstrating that robust accuracy is attainable without missing-value imputation. Full article
(This article belongs to the Special Issue Intelligent Medicine and Health Care, 2nd Edition)
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11 pages, 4142 KiB  
Article
A New Automatic Process Based on Generative Design for CAD Modeling and Manufacturing of Customized Orthosis
by Antonino Cirello, Tommaso Ingrassia, Giuseppe Marannano, Agostino Igor Mirulla, Vincenzo Nigrelli, Giovanni Petrucci and Vito Ricotta
Appl. Sci. 2024, 14(14), 6231; https://doi.org/10.3390/app14146231 - 17 Jul 2024
Cited by 3 | Viewed by 2050
Abstract
As is widely recognized, advancements in new design and rapid prototyping techniques such as CAD modeling and 3D printing are pioneering individualized medicine, facilitating the implementation of new methodologies for creating customized orthoses. The aim of this paper is to develop a new [...] Read more.
As is widely recognized, advancements in new design and rapid prototyping techniques such as CAD modeling and 3D printing are pioneering individualized medicine, facilitating the implementation of new methodologies for creating customized orthoses. The aim of this paper is to develop a new automatic technique for producing personalized orthoses in a straightforward manner, eliminating the necessity for doctors to collaborate directly with technicians. A novel design method for creating customized wrist orthoses has been implemented, notably featuring a generative algorithm for the parametric modeling of the orthosis. To assess the efficacy of the developed algorithm, a case study was conducted involving the design and rapid prototyping of a wrist orthosis using Fused Deposition Modeling (FDM) technology. Subsequently, the developed algorithm was tested by clinicians and patients. The results obtained indicate that the implemented algorithm is user-friendly and could potentially enable non-expert users to design customized orthoses. These results introduce innovative elements of originality within the CAD modeling, offering promising solutions to the challenges associated with the design and production of customized orthoses. Future developments could consist of a better investigation regarding the parameters that influence the accuracy of the scanning and of the printing processes. Full article
(This article belongs to the Special Issue Intelligent Medicine and Health Care, 2nd Edition)
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Review

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23 pages, 23020 KiB  
Review
Artificial Intelligence Algorithms in Asthma Management: A Review of Data Engineering, Predictive Models, and Future Implications
by Shayma Alkobaisi, Muhammad Farhan Safdar, Piotr Pałka and Najah Abed Abu Ali
Appl. Sci. 2025, 15(7), 3609; https://doi.org/10.3390/app15073609 - 25 Mar 2025
Viewed by 1963
Abstract
Asthma is a respiratory condition affecting millions of individuals worldwide, often exacerbated by poor management and worsening weather conditions. As healthcare and weather data continue to expand, identifying the most appropriate and sustainable artificial intelligence (AI) models for asthma care has become a [...] Read more.
Asthma is a respiratory condition affecting millions of individuals worldwide, often exacerbated by poor management and worsening weather conditions. As healthcare and weather data continue to expand, identifying the most appropriate and sustainable artificial intelligence (AI) models for asthma care has become a challenging task. Additionally, the integration of multi-modal data through advanced pre-processing and feature selection techniques has emerged as a critical innovation in developing more effective and robust models. This study examines the current state and potential of AI methods in respiratory care, utilizing available data sources to enhance outcomes. The novelty of this work highlights the progression from classical to advanced models, including machine learning, deep learning, and ChatGPT, applied to diverse data in asthma analysis, while outlining key challenges and discussing potential solutions and future directions. The aim of the study is to highlight how machine learning, deep learning, and hybrid model architectures contribute to effective asthma classification, while also demonstrating ChatGPT’s potential as a reliable support tool for physicians in asthma management and administration. It is projected that the review’s findings on key challenges and opportunities will provide insights and uncover potential research directions in asthma assessment through the application of AI models. Full article
(This article belongs to the Special Issue Intelligent Medicine and Health Care, 2nd Edition)
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18 pages, 1290 KiB  
Review
Normalization of Electrocardiogram-Derived Cardiac Risk Indices: A Scoping Review of the Open-Access Literature
by Erica Iammarino, Ilaria Marcantoni, Agnese Sbrollini, Micaela Morettini and Laura Burattini
Appl. Sci. 2024, 14(20), 9457; https://doi.org/10.3390/app14209457 - 16 Oct 2024
Cited by 2 | Viewed by 1527
Abstract
Changes in cardiac function and morphology are reflected in variations in the electrocardiogram (ECG) and, in turn, in the cardiac risk indices derived from it. These variations have led to the introduction of normalization as a step to compensate for possible biasing factors [...] Read more.
Changes in cardiac function and morphology are reflected in variations in the electrocardiogram (ECG) and, in turn, in the cardiac risk indices derived from it. These variations have led to the introduction of normalization as a step to compensate for possible biasing factors responsible for inter- and intra-subject differences, which can affect the accuracy of ECG-derived risk indices in assessing cardiac risk. The aim of this work is to perform a scoping review to provide a comprehensive collection of open-access published research that examines normalized ECG-derived parameters used as markers of cardiac anomalies or instabilities. The literature search was conducted from February to July 2024 in the major global electronic bibliographic repositories. Overall, 39 studies were selected. Results suggest extensive use of normalization on heart rate variability-related indices (49% of included studies), QT-related indices (18% of included studies), and T-wave alternans (5% of included studies), underscoring their recognized importance and suggesting that normalization may enhance their role as clinically useful risk markers. However, the primary objective of the included studies was not to evaluate the effect of normalization itself; thus, further research is needed to definitively assess the impact and advantages of normalization across various ECG-derived parameters. Full article
(This article belongs to the Special Issue Intelligent Medicine and Health Care, 2nd Edition)
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