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Technologies
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Application of Artificial Intelligence in Medical Image Analysis
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Application of Artificial Intelligence in Medical Image Analysis

A special issue of Technologies (ISSN 2227-7080). This special issue belongs to the section "Information and Communication Technologies".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 1667

Special Issue Editors

Dr. Masateru Kawakubo

E-Mail Website
Guest Editor
Faculty of Medical Sciences, Kyushu University, Fukuoka, Japan
Interests: medical image; deep learning in image
Prof. Dr. Tamás Haidegger

E-Mail Website
Guest Editor
Antal Bejczy Center for Intelligent Robotics, Obuda University, Budapest, Hungary
Interests: surgical robotics; medical robot autonomy; robot safety and standardization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Happy New Year 2025! As we welcome the new year, we are thrilled to celebrate the publication of a Special Issue of Technologies (ISSN: 2227-7080) focused on the ‘Application of Artificial Intelligence in Medical Image Analysis’. This issue highlights the transformative potential of AI in advancing medical imaging and healthcare, and we would like to invite you to join us in exploring this dynamic and impactful field.

In recent years, the integration of artificial intelligence (AI) in medical imaging has revolutionized diagnostic precision, therapeutic planning, and healthcare delivery. This Special Issue aims to serve as a comprehensive platform for presenting cutting-edge research and fostering collaboration across academia, industry, and clinical practice. We believe that this collection of articles will not only showcase the latest advancements but also inspire future innovations in this dynamic field.

We invite researchers, clinicians, and technologists to contribute original research papers or comprehensive review articles on topics including, but not limited to, the following:

  • AI-driven diagnostic tools and predictive models in medical imaging: the development and application of artificial intelligence, including deep learning and machine learning, to enhance diagnostic accuracy and predictive capabilities in clinical practice.・ Radiomics and AI integration: combining radiomics with AI methodologies to extract quantitative imaging features and improve disease characterization, prognosis, and treatment response prediction.
  • Automated image segmentation and medical image reconstruction: leveraging AI-driven algorithms for the precise and efficient segmentation of medical images and advanced reconstruction techniques to improve image quality and diagnostic reliability.
  • Multi-modal image analysis for enhanced diagnostic accuracy: integrating data from multiple imaging modalities (e.g., CT, MRI, US, PET) using AI to provide comprehensive insights and support clinical decision making.
  • Applications of deep learning and machine learning in medical image processing: advancements in neural network architectures and their implementation in tasks such as classification, anomaly detection, and feature extraction.
  • Real-world data (RWD) science in AI-driven medical imaging: harnessing large-scale, real-world datasets to train and validate AI models, addressing challenges like data heterogeneity and clinical applicability.
  • Ethical considerations, validation, and standardization of AI technologies: ensuring transparency, fairness, and clinical relevance in AI model development and deployment, with a focus on regulatory and ethical compliance.

The scope of this Special Issue reflects the diversity and complexity of AI applications in medical imaging, encompassing advancements in deep learning, machine learning, and AI-driven diagnostics. It addresses critical challenges such as data quality, automated image segmentation, algorithm transparency, and clinical implementation. By highlighting innovations in radiomics and AI integration, medical image reconstruction, and multi-modal image analysis, this issue aims to bridge gaps between research, technology development, and clinical practice.

Through contributions from interdisciplinary fields, we aspire to present a holistic perspective on the current state and future prospects of AI in medical imaging, inspiring transformative solutions for enhanced healthcare delivery.

We look forward to receiving your contributions and collaborating with you to make this Special Issue a landmark in advancing medical imaging and AI innovation.

Dr. Masateru Kawakubo
Prof. Dr. Tamás Haidegger
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. Technologies is an international peer-reviewed open access monthly 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 1600 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

  • deep learning
  • machine learning
  • AI-driven diagnostics
  • radiomics and AI integration
  • automated
  • image segmentation
  • medical image reconstruction
  • multi-modal image analysis

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Published Papers (3 papers)

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Research

16 pages, 5302 KiB  
Article
BREAST-CAD: A Computer-Aided Diagnosis System for Breast Cancer Detection Using Machine Learning
by Riyam M. Masoud, Ramadan Madi Ali Bakir, M. Sabry Saraya and Sarah M. Ayyad
Technologies 2025, 13(7), 268; https://doi.org/10.3390/technologies13070268 - 24 Jun 2025
Viewed by 167
Abstract
This research presents a novel Computer-Aided Diagnosis (CAD) system called BREAST-CAD, developed to support clinicians in breast cancer detection. Our approach follows a three-phase methodology: Initially, a comprehensive literature review between 2000 and 2024 informed the choice of a suitable dataset and the [...] Read more.
This research presents a novel Computer-Aided Diagnosis (CAD) system called BREAST-CAD, developed to support clinicians in breast cancer detection. Our approach follows a three-phase methodology: Initially, a comprehensive literature review between 2000 and 2024 informed the choice of a suitable dataset and the selection of Naive Bayes (NB), K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Decision Trees (DT) Machine Learning (ML) algorithms. Subsequently, the dataset was preprocessed and the four ML models were trained and validated, with the DT model achieving superior accuracy. We developed a novel, integrated client–server architecture for real-time diagnostic support, an aspect often underexplored in the current CAD literature. In the final phase, the DT model was embedded within a user-friendly client application, empowering clinicians to input patient diagnostic data directly and receive immediate, AI-driven predictions of cancer probability, with results securely transmitted and managed by a dedicated server, facilitating remote access and centralized data storage and ensuring data integrity. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Medical Image Analysis)
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21 pages, 1062 KiB  
Article
Red-KPLS Feature Reduction with 1D-ResNet50: Deep Learning Approach for Multiclass Alzheimer’s Staging
by Syrine Neffati, Ameni Filali, Kawther Mekki and Kais Bouzrara
Technologies 2025, 13(6), 258; https://doi.org/10.3390/technologies13060258 - 19 Jun 2025
Viewed by 254
Abstract
The early detection of Alzheimer’s disease (AD) is essential for improving patient outcomes, enabling timely intervention, and slowing disease progression. However, the complexity of neuroimaging data presents significant obstacles to accurate classification. This study introduces a computationally efficient AI framework designed to enhance [...] Read more.
The early detection of Alzheimer’s disease (AD) is essential for improving patient outcomes, enabling timely intervention, and slowing disease progression. However, the complexity of neuroimaging data presents significant obstacles to accurate classification. This study introduces a computationally efficient AI framework designed to enhance AD staging using structural MRI. The proposed method integrates discrete wavelet transform (DWT) for multi-scale feature extraction, a novel reduced kernel partial least squares (Red-KPLS) algorithm for feature reduction, and ResNet-50 for classification. The proposed technique, referred to as Red-KPLS-CNN, refines MRI features into discriminative biomarkers while minimizing redundancy. As a result, the framework achieves 96.9% accuracy and an F1-score of 97.8% in the multiclass classification of AD cases using the Kaggle dataset. The dataset was strategically partitioned into 60% training, 20% validation, and 20% testing sets, preserving class balance throughout all splits. The integration of Red–KPLS enhances feature selection, reducing dimensionality without compromising diagnostic sensitivity. Compared to conventional models, our approach improves classification robustness and generalization, reinforcing its potential for scalable and interpretable AD diagnostics. These findings emphasize the importance of hybrid wavelet–kernel–deep learning architectures, offering a promising direction for advancing computer-aided diagnosis (CAD) in clinical applications. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Medical Image Analysis)
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15 pages, 4095 KiB  
Article
AI-Generated Mnemonic Images Improve Long-Term Retention of Coronary Artery Occlusions in STEMI: A Comparative Study
by Zahraa Alomar, Meize Guo and Tyler Bland
Technologies 2025, 13(6), 217; https://doi.org/10.3390/technologies13060217 - 26 May 2025
Cited by 1 | Viewed by 453
Abstract
Medical students face significant challenges retaining complex information, such as interpreting ECGs for coronary artery occlusions, amidst demanding curricula. While artificial intelligence (AI) is increasingly used for medical image analysis, this study explored using generative AI (DALLE-3) to create mnemonic-based images to enhance [...] Read more.
Medical students face significant challenges retaining complex information, such as interpreting ECGs for coronary artery occlusions, amidst demanding curricula. While artificial intelligence (AI) is increasingly used for medical image analysis, this study explored using generative AI (DALLE-3) to create mnemonic-based images to enhance human learning and retention of medical images, in particular, electrocardiograms (ECGs). This study is among the first to investigate generative AI as a tool not for automated diagnosis but as a human-centered educational aid designed to enhance long-term retention in complex visual tasks like ECG interpretation. We conducted a comparative study with 275 first-year medical students across six campuses; an experimental group (n = 40) received a lecture supplemented with AI-generated mnemonic ECG images, while control groups (n = 235) received standard lectures with traditional ECG diagrams. Student achievement and retention were assessed by course examinations, and student preference and engagement were measured using the Situational Interest Survey for Multimedia (SIS-M). Control groups showed a significant decline in scores on the relevant exam question over time, whereas the experimental group’s scores remained stable, indicating improved long-term retention. Experimental students also reported significantly higher situational interest in the mnemonic-based images over traditional images. AI-generated mnemonic images can effectively improve long-term retention of complex ECG interpretation skills and enhance student engagement and preference, highlighting generative AI’s potential as a valuable cognitive tool in image analysis during medical education. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Medical Image Analysis)
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