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Artificial Intelligence and Machine Learning for Improving Medical Treatment and Healthcare Systems

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

Deadline for manuscript submissions: 20 November 2024 | Viewed by 3362

Special Issue Editors

Dr. Giacomo Cappon

E-Mail Website
Guest Editor
Department of Information Engineering, University of Padova, Padova, Italy
Interests: machine learning; Bayesian methods; signal processing; decision support systems; wearable sensors; digital health and therapeutics; telemedicine
Special Issues, Collections and Topics in MDPI journals
Dr. Martina Vettoretti

E-Mail Website
Guest Editor
Department of Information Engineering (DEI), University of Padova, via Gradenigo 6B, 35131 Padova, Italy
Interests: artificial intelligence; machine learning; signal processing; modeling and simulation techniques; wearable sensors; predictive models of health risks in various applications regarding the prevention and treatment of chronic diseases (e.g., diabetes, respiratory diseases and neurodegenerative diseases)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce a Special Issue titled "Artificial Intelligence and Machine Learning for Improving Medical Treatment and Healthcare Systems." As the field of healthcare undergoes transformative changes driven by the advancements in artificial intelligence (AI) and machine learning (ML), we cordially invite you to contribute your cutting-edge research to this groundbreaking Issue.

This Special Issue aims to showcase the latest breakthroughs, innovative methodologies, and promising applications of AI and ML in revolutionizing medical treatment and healthcare systems. We seek high-quality contributions encompassing a wide range of topics, including but not limited to AI-driven diagnostic tools, predictive modeling, personalized medicine, data mining and analysis, clinical decision support systems, image and signal processing, and healthcare resource management.

By sharing your research, you will have the opportunity to disseminate your findings to a global audience of renowned experts in the field, fostering collaborations and stimulating further advancements in the intersection of AI, ML, and healthcare. This Special Issue will serve as a platform for thought-provoking discussions, encouraging the exchange of ideas and paving the way for impactful solutions that can enhance patient care and improve healthcare outcomes.

We invite original research articles, review papers, case studies, and perspectives that explore the full potential of AI and ML in medical treatment and healthcare systems. All submissions will undergo a rigorous peer-review process to ensure the highest scientific standards.

Topics of interest include, but are not limited to:

  • AI-driven diagnostic tools for accurate disease detection;
  • Predictive modeling and forecasting for healthcare outcomes;
  • Personalized medicine approaches using AI and ML;
  • Data mining and analysis techniques for healthcare data;
  • Clinical decision support systems based on AI and ML algorithms;
  • Image and signal processing advancements in medical imaging;
  • Natural language processing for automated medical record analysis;
  • ML techniques for identifying disease biomarkers and genetic associations;
  • Machine learning in healthcare resource management and optimization;
  • AI-enabled wearable devices for remote patient monitoring;
  • Deep learning algorithms for medical image interpretation and analysis;
  • Ethical considerations and bias mitigation in AI and ML applications in healthcare;
  • Explainable AI for transparency and trust in medical decision-making.

Dr. Giacomo Cappon
Dr. Martina Vettoretti
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

  • advanced healthcare methods
  • personalized medicine
  • explainable artificial intelligence and machine learning
  • decision support systems
  • advanced algorithms in medicine
  • diagnostic artificial intelligence and machine learning

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

Published Papers (2 papers)

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Research

17 pages, 4274 KiB  
Article
Improved Brain Tumor Segmentation in MR Images with a Modified U-Net
by Hiam Alquran, Mohammed Alslatie, Ali Rababah and Wan Azani Mustafa
Appl. Sci. 2024, 14(15), 6504; https://doi.org/10.3390/app14156504 - 25 Jul 2024
Viewed by 1440
Abstract
Detecting brain tumors is crucial in medical diagnostics due to the serious health risks these abnormalities present to patients. Deep learning approaches can significantly improve localization in various medical issues, particularly brain tumors. This paper emphasizes the use of deep learning models to [...] Read more.
Detecting brain tumors is crucial in medical diagnostics due to the serious health risks these abnormalities present to patients. Deep learning approaches can significantly improve localization in various medical issues, particularly brain tumors. This paper emphasizes the use of deep learning models to segment brain tumors using a large dataset. The study involves comparing modifications to U-Net structures, including kernel size, number of channels, dropout ratio, and changing the activation function from ReLU to Leaky ReLU. Optimizing these parameters has notably enhanced brain tumor segmentation in MR images, achieving a Global Accuracy of 99.4% and a dice similarity coefficient of 90.2%. The model was trained, validated, and tested on many magnetic resonance images, with a training time not exceeding 19 min on a powerful GPU. This approach can be extended in medical care and hospitals to assist radiologists in identifying tumor locations and suspicious regions, thereby improving diagnosis and treatment effectiveness. The software could also be integrated into MR equipment protocols. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning for Improving Medical Treatment and Healthcare Systems)
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23 pages, 14703 KiB  
Article
Precision Diagnosis of Glaucoma with VLLM Ensemble Deep Learning
by Soohyun Wang, Byoungkug Kim, Jiheon Kang and Doo-Seop Eom
Appl. Sci. 2024, 14(11), 4588; https://doi.org/10.3390/app14114588 - 27 May 2024
Viewed by 1142
Abstract
This paper focuses on improving automated approaches to glaucoma diagnosis, a severe disease that leads to gradually narrowing vision and potentially blindness due to optic nerve damage occurring without the patient’s awareness. Early diagnosis is crucial. By utilizing advanced deep learning technologies and [...] Read more.
This paper focuses on improving automated approaches to glaucoma diagnosis, a severe disease that leads to gradually narrowing vision and potentially blindness due to optic nerve damage occurring without the patient’s awareness. Early diagnosis is crucial. By utilizing advanced deep learning technologies and robust image processing capabilities, this study employed four types of input data (retina fundus image, region of interest (ROI), vascular region of interest (VROI), and color palette images) to reflect structural issues. We addressed the issue of data imbalance with a modified loss function and proposed an ensemble model based on the vision large language model (VLLM), which improved the accuracy of glaucoma classification. The results showed that the models developed for each dataset achieved 1% to 10% higher accuracy and 8% to 29% improved sensitivity compared to conventional single-image analysis. On the REFUGE dataset, we achieved a high accuracy of 0.9875 and a sensitivity of 0.9. Particularly in the ORIGA dataset, which is challenging in terms of achieving high accuracy, we confirmed a significant increase, with an 11% improvement in accuracy and a 29% increase in sensitivity. This research can significantly contribute to the early detection and management of glaucoma, indicating potential clinical applications. These advancements will not only further the development of glaucoma diagnostic technologies but also play a vital role in improving patients’ quality of life. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning for Improving Medical Treatment and Healthcare Systems)
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