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Artificial Intelligence Applications in Healthcare System
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Artificial Intelligence Applications in Healthcare System

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

Deadline for manuscript submissions: closed (30 November 2024) | Viewed by 8608

Special Issue Editors

Dr. Ayman Ibaida

E-Mail Website
Guest Editor
College of Arts, Business, Law, Education & IT, Victoria University, Melbourne, VIC 3001, Australia
Interests: AI; healthcare; ECG; NLP; medical imaging
Special Issues, Collections and Topics in MDPI journals
Dr. Khandakar Ahmed

E-Mail Website
Guest Editor
College of Engineering and Science, Victoria University, Footscray, VIC 3011, Australia
Interests: machine learning; biomedical informatics; Internet of Things; smart technology and cybersecurity
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are inviting submissions to a Special Issue of Applied Sciences entitled “Artificial Intelligence Applications in Healthcare System".

This Special Issue will explore the transformative impact of AI in healthcare. It welcomes research and insights into AI-driven clinical decision support, healthcare data analytics, medical imaging, natural language processing, telemedicine, drug discovery, ethics, robotics, and public health applications. By addressing these topics, the Issue aims to advance the integration of AI into healthcare, fostering improved patient care, efficient processes, and medical breakthroughs. It invites contributions from researchers and practitioners to share their innovative work, guiding the ethical and regulatory considerations that are crucial to this evolving field. Join us in shaping the future of healthcare through the power of artificial intelligence.

Dr. Ayman Ibaida
Dr. Khandakar Ahmed
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

  • clinical decision support
  • healthcare data analytics
  • medical imaging
  • telemedicine
  • drug discovery
  • medical diagnosis
  • healthcare automation

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Related Special Issue

Published Papers (4 papers)

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Research

11 pages, 1626 KiB  
Article
Graph Neural Networks for Analyzing Trauma-Related Brain Structure in Children and Adolescents: A Pilot Study
by Harim Jeong, Minjoo Kang, Shanon McLeay, R. J. R. Blair, Unsun Chung and Soonjo Hwang
Appl. Sci. 2025, 15(1), 277; https://doi.org/10.3390/app15010277 - 31 Dec 2024
Viewed by 1021
Abstract
This study explores the potential of graph neural networks (GNNs) in analyzing brain networks of children and adolescents exposed to trauma, addressing limitations in traditional neuroimaging approaches. MRI-based brain data from trauma-exposed and control groups were modeled as whole-brain networks using regions-of-interest (ROIs), [...] Read more.
This study explores the potential of graph neural networks (GNNs) in analyzing brain networks of children and adolescents exposed to trauma, addressing limitations in traditional neuroimaging approaches. MRI-based brain data from trauma-exposed and control groups were modeled as whole-brain networks using regions-of-interest (ROIs), with GNNs applied to capture complex, non-linear connectivity patterns. Results revealed that the trauma-exposed group exhibited simplified network structures with higher importance in regions associated with cognitive and emotional regulation, such as the posterior cerebellum. In contrast, the control group demonstrated richer connectivity patterns, emphasizing regions related to motor and visual processing, such as the Right Lingual Gyrus. Compared to traditional t-test results highlighting regional density differences, the GNN approach uncovered deeper, network-level insights into the relationships between brain regions. These findings demonstrate the utility of GNNs in advancing neuroimaging research, offering new perspectives on trauma’s impact on brain connectivity and paving the way for future applications in understanding neural mechanisms and interventions. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications in Healthcare System)
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22 pages, 3066 KiB  
Article
Is Mild Really Mild?: Generating Longitudinal Profiles of Stroke Survivor Impairment and Impact Using Unsupervised Machine Learning
by Achini Adikari, Rashmika Nawaratne, Daswin De Silva, David L. Carey, Alistair Walsh, Carolyn Baum, Stephen Davis, Geoffrey A. Donnan, Damminda Alahakoon and Leeanne M. Carey
Appl. Sci. 2024, 14(15), 6800; https://doi.org/10.3390/app14156800 - 4 Aug 2024
Cited by 1 | Viewed by 1505
Abstract
The National Institute of Health Stroke Scale (NIHSS) is used worldwide to classify stroke severity as ‘mild’, ‘moderate’, or ‘severe’ based on neurological impairment. Yet, stroke survivors argue that the classification of ‘mild’ does not represent the holistic experience and impact of stroke [...] Read more.
The National Institute of Health Stroke Scale (NIHSS) is used worldwide to classify stroke severity as ‘mild’, ‘moderate’, or ‘severe’ based on neurological impairment. Yet, stroke survivors argue that the classification of ‘mild’ does not represent the holistic experience and impact of stroke on their daily lives. In this observational cohort study, we aimed to identify different types of impairment profiles among stroke survivors classified as ‘mild’. We used survivors of mild stroke’ data from the START longitudinal stroke cohort (n = 73), with measures related to sensorimotor, cognition, depression, functional disability, physical activity, work, and social adjustment over 12 months. Given the multisource, multigranular, and unlabeled nature of the data, we utilized a structure-adapting, unsupervised machine learning approach, the growing self-organizing map (GSOM) algorithm, to generate distinct clinical profiles. These diverse impairment profiles revealed that survivors of mild stroke experience varying degrees of impairment and impact (cognitive, depression, physical activity, work/social adjustment) at different time points, despite the uniformity implied by their NIHSS-classified ‘mild’ stroke. This emphasizes the necessity of creating a holistic and more comprehensive representation of survivors of mild stroke’ needs over the first year after stroke to improve rehabilitation and poststroke care. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications in Healthcare System)
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24 pages, 656 KiB  
Article
From Posts to Knowledge: Annotating a Pandemic-Era Reddit Dataset to Navigate Mental Health Narratives
by Saima Rani, Khandakar Ahmed and Sudha Subramani
Appl. Sci. 2024, 14(4), 1547; https://doi.org/10.3390/app14041547 - 15 Feb 2024
Cited by 1 | Viewed by 2945
Abstract
Mental illness is increasingly recognized as a substantial public health challenge worldwide. With the advent of social media, these platforms have become pivotal for individuals to express their emotions, thoughts, and experiences, thereby serving as a rich resource for mental health research. This [...] Read more.
Mental illness is increasingly recognized as a substantial public health challenge worldwide. With the advent of social media, these platforms have become pivotal for individuals to express their emotions, thoughts, and experiences, thereby serving as a rich resource for mental health research. This paper is devoted to the creation of a comprehensive dataset and an innovative data annotation methodology to explore the underlying causes of these mental health issues. Our approach included the extraction of over one million Reddit posts from five different subreddits, spanning the pre-pandemic, during-pandemic, and post-pandemic periods. These posts were methodically annotated using a set of specific criteria, aimed at identifying various root causes. This rigorous process produced a richly categorized dataset, invaluable for detailed analysis. The complete unlabelled dataset, along with a subset that has been expertly annotated, is prepared for public release, as outlined in the data availability section. This dataset is a critical resource for training and fine-tuning machine learning models to identify the foundational triggers of individual mental health issues, offering valuable insights for practical interventions and future research in this domain. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications in Healthcare System)
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18 pages, 3167 KiB  
Article
Ensemble Learning-Based Coronary Artery Disease Detection Using Computer Tomography Images
by Abdul Rahaman Wahab Sait and Ali Mohammad Alorsan Bani Awad
Appl. Sci. 2024, 14(3), 1238; https://doi.org/10.3390/app14031238 - 1 Feb 2024
Cited by 1 | Viewed by 2280
Abstract
Coronary artery disease (CAD) is the most prevalent form of cardiovascular disease that may result in myocardial infarction. Annually, it leads to millions of fatalities and causes billions of dollars in global economic losses. Limited resources and complexities in interpreting results pose challenges [...] Read more.
Coronary artery disease (CAD) is the most prevalent form of cardiovascular disease that may result in myocardial infarction. Annually, it leads to millions of fatalities and causes billions of dollars in global economic losses. Limited resources and complexities in interpreting results pose challenges to healthcare centers in implementing deep learning (DL)-based CAD detection models. Ensemble learning (EL) allows developers to build an effective CAD detection model by integrating the outcomes of multiple medical imaging models. In this study, the authors build an EL-based CAD detection model to identify CAD from coronary computer tomography angiography (CCTA) images. They employ a feature engineering technique, including MobileNet V3, CatBoost, and LightGBM models. A random forest (RF) classifier is used to ensemble the outcomes of the CatBoost and LightGBM models. The authors generalize the model using two benchmark datasets. The proposed model achieved an accuracy of 99.7% and 99.6% with limited computational resources. The generalization results highlight the importance of the proposed model’s efficiency in identifying CAD from the CCTA images. Healthcare centers and cardiologists can benefit from the proposed model to identify CAD in the initial stages. The proposed feature engineering can be extended using a liquid neural network model to reduce computational resources. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications in Healthcare System)
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