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Diagnostics
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Artificial Intelligence Advances for Medical Computer-Aided Diagnosis—2nd Edition
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Artificial Intelligence Advances for Medical Computer-Aided Diagnosis—2nd Edition

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 5145

Special Issue Editor

Dr. Mugahed A. Al-antari

E-Mail Website
Guest Editor
Department of Artificial Intelligence, College of Software and Convergence Technology, Daeyang AI Center, Sejong University, Seoul 05006, Republic of Korea
Interests: image classification; image segmentation; medical image processing; biomedical optical imaging; medical signal processing; artificial intelligence; deep learning; machine learning; computer-aided diagnosis; explainable artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

A computer-aided diagnosis (CAD) system involves various stages such as detection, segmentation and classification. Over the last few decades, computer-aided diagnosis systems have become a part of clinical practice. They have the potential to assist clinicians in daily diagnostic tasks. The image processing techniques are fast, repeatable and robust, which helps physicians detect, classify, segment and measure various structures. Medical experts rely on medical imaging modalities such as computed tomography (CT), microscopic blood smear images, magnetic resonance imaging (MRI), X-ray and ultrasound (US) to diagnose health challenges and assign treatment prescriptions. Researchers and developers are able to deliver smart solutions for medical imaging diagnoses thanks to the AI-based potential functionalities of machine learning and deep learning technologies.

In this Special Issue, we will cover original articles, short communication and reviews related to various deep learning techniques and computer-aided diagnoses for biomedical systems. We invite all potential authors to submit their research contributions to explore the possible methodologies and techniques for the healthcare environment.

This Special Issue is dedicated to high-quality, original research papers in the overlapping fields of the following:

AI-based medical image diagnosis;

Medical deep learning CAD systems;

XAI-based medical imaging;

Medical image/bio-signal analysis;

Medical image segmentation;

Medical image segmentation;

Hybrid medical knowledge generation;

Deep reinforcement learning;

Healthcare systems;

AI-based prognosis and recommendations.

Dr. Mugahed A. Al-antari
Guest Editor

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. Diagnostics 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 2600 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

  • image classification
  • image segmentation
  • medical image processing
  • biomedical optical imaging
  • medical signal processing
  • artificial intelligence
  • deep learning
  • machine learning
  • computer-aided diagnosis
  • explainable artificial intelligence

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Published Papers (5 papers)

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Research

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19 pages, 3110 KiB  
Article
Improving Imitation Skills in Children with Autism Spectrum Disorder Using the NAO Robot and a Human Action Recognition
by Abeer Alnafjan, Maha Alghamdi, Noura Alhakbani and Yousef Al-Ohali
Diagnostics 2025, 15(1), 60; https://doi.org/10.3390/diagnostics15010060 - 29 Dec 2024
Viewed by 976
Abstract
Background/Objectives: Autism spectrum disorder (ASD) is a group of developmental disorders characterized by poor social skills, low motivation in activities, and a lack of interaction with others. Traditional intervention approaches typically require support under the direct supervision of well-trained professionals. However, teaching and [...] Read more.
Background/Objectives: Autism spectrum disorder (ASD) is a group of developmental disorders characterized by poor social skills, low motivation in activities, and a lack of interaction with others. Traditional intervention approaches typically require support under the direct supervision of well-trained professionals. However, teaching and training programs for children with ASD can also be enhanced by assistive technologies, artificial intelligence, and robotics. Methods: In this study, we examined whether robotics can improve the imitation skills of children with autism and support therapists during therapeutic sessions. We designed scenarios for training hand clapping imitation skills using the NAO robot and analyzed the interaction between children with autism and the robot. Results: We developed a deep learning approach based on the human action recognition algorithm for analyzing clapping imitation. Conclusions: Our findings suggest that integrating robotics into therapeutic practices can effectively enhance the imitation skills of children with ASD, offering valuable support to therapists. Full article
(This article belongs to the Special Issue Artificial Intelligence Advances for Medical Computer-Aided Diagnosis—2nd Edition)
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17 pages, 3300 KiB  
Article
Minimum and Maximum Pattern-Based Self-Organized Feature Engineering: Fibromyalgia Detection Using Electrocardiogram Signals
by Veysel Yusuf Cambay, Abdul Hafeez Baig, Emrah Aydemir, Turker Tuncer and Sengul Dogan
Diagnostics 2024, 14(23), 2708; https://doi.org/10.3390/diagnostics14232708 - 30 Nov 2024
Cited by 1 | Viewed by 639
Abstract
Background: The primary objective of this research is to propose a new, simple, and effective feature extraction function and to investigate its classification ability using electrocardiogram (ECG) signals. Methods: In this research, we present a new and simple feature extraction function named the [...] Read more.
Background: The primary objective of this research is to propose a new, simple, and effective feature extraction function and to investigate its classification ability using electrocardiogram (ECG) signals. Methods: In this research, we present a new and simple feature extraction function named the minimum and maximum pattern (MinMaxPat). In the proposed MinMaxPat, the signal is divided into overlapping blocks with a length of 16, and the indexes of the minimum and maximum values are identified. Then, using the computed indices, a feature map is calculated in base 16, and the histogram of the generated map is extracted to obtain the feature vector. The length of the generated feature vector is 256. To evaluate the classification ability of this feature extraction function, we present a new feature engineering model with three main phases: (i) feature extraction using MinMaxPat, (ii) cumulative weight-based iterative neighborhood component analysis (CWINCA)-based feature selection, and (iii) classification using a t-algorithm-based k-nearest neighbors (tkNN) classifier. Results: To obtain results, we applied the proposed MinMaxPat-based feature engineering model to a publicly available ECG fibromyalgia dataset. Using this dataset, three cases were analyzed, and the proposed MinMaxPat-based model achieved over 80% classification accuracy with both leave-one-record-out (LORO) cross-validation (CV) and 10-fold CV. Conclusions: These results clearly demonstrate that this simple model achieved high classification performance. Therefore, this model is surprisingly effective for ECG signal classification. Full article
(This article belongs to the Special Issue Artificial Intelligence Advances for Medical Computer-Aided Diagnosis—2nd Edition)
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14 pages, 12366 KiB  
Article
Enhancing Accuracy in Breast Density Assessment Using Deep Learning: A Multicentric, Multi-Reader Study
by Marek Biroš, Daniel Kvak, Jakub Dandár, Robert Hrubý, Eva Janů, Anora Atakhanova and Mugahed A. Al-antari
Diagnostics 2024, 14(11), 1117; https://doi.org/10.3390/diagnostics14111117 - 28 May 2024
Viewed by 1591
Abstract
The evaluation of mammographic breast density, a critical indicator of breast cancer risk, is traditionally performed by radiologists via visual inspection of mammography images, utilizing the Breast Imaging-Reporting and Data System (BI-RADS) breast density categories. However, this method is subject to substantial interobserver [...] Read more.
The evaluation of mammographic breast density, a critical indicator of breast cancer risk, is traditionally performed by radiologists via visual inspection of mammography images, utilizing the Breast Imaging-Reporting and Data System (BI-RADS) breast density categories. However, this method is subject to substantial interobserver variability, leading to inconsistencies and potential inaccuracies in density assessment and subsequent risk estimations. To address this, we present a deep learning-based automatic detection algorithm (DLAD) designed for the automated evaluation of breast density. Our multicentric, multi-reader study leverages a diverse dataset of 122 full-field digital mammography studies (488 images in CC and MLO projections) sourced from three institutions. We invited two experienced radiologists to conduct a retrospective analysis, establishing a ground truth for 72 mammography studies (BI-RADS class A: 18, BI-RADS class B: 43, BI-RADS class C: 7, BI-RADS class D: 4). The efficacy of the DLAD was then compared to the performance of five independent radiologists with varying levels of experience. The DLAD showed robust performance, achieving an accuracy of 0.819 (95% CI: 0.736–0.903), along with an F1 score of 0.798 (0.594–0.905), precision of 0.806 (0.596–0.896), recall of 0.830 (0.650–0.946), and a Cohen’s Kappa (κ) of 0.708 (0.562–0.841). The algorithm achieved robust performance that matches and in four cases exceeds that of individual radiologists. The statistical analysis did not reveal a significant difference in accuracy between DLAD and the radiologists, underscoring the model’s competitive diagnostic alignment with professional radiologist assessments. These results demonstrate that the deep learning-based automatic detection algorithm can enhance the accuracy and consistency of breast density assessments, offering a reliable tool for improving breast cancer screening outcomes. Full article
(This article belongs to the Special Issue Artificial Intelligence Advances for Medical Computer-Aided Diagnosis—2nd Edition)
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Review

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19 pages, 995 KiB  
Review
A Systematic Integration of Artificial Intelligence Models in Appendicitis Management: A Comprehensive Review
by Ivan Maleš, Marko Kumrić, Andrea Huić Maleš, Ivan Cvitković, Roko Šantić, Zenon Pogorelić and Joško Božić
Diagnostics 2025, 15(7), 866; https://doi.org/10.3390/diagnostics15070866 - 28 Mar 2025
Viewed by 119
Abstract
Artificial intelligence (AI) and machine learning (ML) are transforming the management of acute appendicitis by enhancing diagnostic accuracy, optimizing treatment strategies, and improving patient outcomes. This study reviews AI applications across all stages of appendicitis care, from triage to postoperative management, using sources [...] Read more.
Artificial intelligence (AI) and machine learning (ML) are transforming the management of acute appendicitis by enhancing diagnostic accuracy, optimizing treatment strategies, and improving patient outcomes. This study reviews AI applications across all stages of appendicitis care, from triage to postoperative management, using sources from PubMed/MEDLINE, IEEE Xplore, arXiv, Web of Science, and Scopus, covering publications up to 14 February 2025. AI models have demonstrated potential in triage, enabling rapid differentiation of appendicitis from other causes of abdominal pain. In diagnostics, ML algorithms incorporating clinical, laboratory, imaging, and demographic data have improved accuracy and reduced uncertainty. These tools also predict disease severity, aiding decisions between conservative management and surgery. Radiomics further enhances diagnostic precision by analyzing imaging data. Intraoperatively, AI applications are emerging to support real-time decision-making, assess procedural steps, and improve surgical training. Postoperatively, ML models predict complications such as abscess formation and sepsis, facilitating early interventions and personalized recovery plans. This is the first comprehensive review to examine AI’s role across the entire appendicitis treatment process, including triage, diagnosis, severity prediction, intraoperative assistance, and postoperative prognosis. Despite its potential, challenges remain regarding data quality, model interpretability, ethical considerations, and clinical integration. Future efforts should focus on developing end-to-end AI-assisted workflows that enhance diagnosis, treatment, and patient outcomes while ensuring equitable access and clinician oversight. Full article
(This article belongs to the Special Issue Artificial Intelligence Advances for Medical Computer-Aided Diagnosis—2nd Edition)
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28 pages, 2266 KiB  
Review
Explainable Artificial Intelligence in Neuroimaging of Alzheimer’s Disease
by Mahdieh Taiyeb Khosroshahi, Soroush Morsali, Sohrab Gharakhanlou, Alireza Motamedi, Saeid Hassanbaghlou, Hadi Vahedi, Siamak Pedrammehr, Hussain Mohammed Dipu Kabir and Ali Jafarizadeh
Diagnostics 2025, 15(5), 612; https://doi.org/10.3390/diagnostics15050612 - 4 Mar 2025
Viewed by 641
Abstract
Alzheimer’s disease (AD) remains a significant global health challenge, affecting millions worldwide and imposing substantial burdens on healthcare systems. Advances in artificial intelligence (AI), particularly in deep learning and machine learning, have revolutionized neuroimaging-based AD diagnosis. However, the complexity and lack of interpretability [...] Read more.
Alzheimer’s disease (AD) remains a significant global health challenge, affecting millions worldwide and imposing substantial burdens on healthcare systems. Advances in artificial intelligence (AI), particularly in deep learning and machine learning, have revolutionized neuroimaging-based AD diagnosis. However, the complexity and lack of interpretability of these models limit their clinical applicability. Explainable Artificial Intelligence (XAI) addresses this challenge by providing insights into model decision-making, enhancing transparency, and fostering trust in AI-driven diagnostics. This review explores the role of XAI in AD neuroimaging, highlighting key techniques such as SHAP, LIME, Grad-CAM, and Layer-wise Relevance Propagation (LRP). We examine their applications in identifying critical biomarkers, tracking disease progression, and distinguishing AD stages using various imaging modalities, including MRI and PET. Additionally, we discuss current challenges, including dataset limitations, regulatory concerns, and standardization issues, and propose future research directions to improve XAI’s integration into clinical practice. By bridging the gap between AI and clinical interpretability, XAI holds the potential to refine AD diagnostics, personalize treatment strategies, and advance neuroimaging-based research. Full article
(This article belongs to the Special Issue Artificial Intelligence Advances for Medical Computer-Aided Diagnosis—2nd Edition)
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