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Advanced Artificial Intelligence Techniques for Disease Prediction, Diagnosis and Management

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A special issue of Eng (ISSN 2673-4117).

Deadline for manuscript submissions: 31 March 2026 | Viewed by 12261

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Special Issue Editor

Dr. Hisham Daoud

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Guest Editor

School of Computer and Cyber Sciences, Augusta University, Augusta, GA, USA
Interests: biomedical signal processing; medical imaging; machine learning; deep learning; neuromorphic computing

Special Issue Information

Dear Colleagues,

Artificial intelligence has revolutionized healthcare by changing how diseases are diagnosed and managed. This technology is not only enhancing the precision of diagnoses but also enabling disease prediction and personalized treatment plans. Through accurate diagnosis and more personalized therapy, AI is significantly improving healthcare research and patient outcomes. The capacity of AI in healthcare to rapidly assess massive amounts of clinical data enables physicians to identify abnormalities and disease biomarkers that would otherwise be undetected. Recognizing and leveraging these transformative technologies to enhance patient care is crucial. As biomedical informatics continues to evolve, there is a growing need to integrate AI-driven approaches into healthcare.

This Special Issue invites submissions presenting solutions focusing on cutting-edge AI methodologies applied to various medical challenges. The issue will feature research on AI-driven disease prediction, diagnosis, and management, leveraging electronic health records, genomics, medical imaging, and wearable sensor data to inform clinical decision-making. Topics of this Special Issue include, but are not restricted to, the following:

AI-based clinical decision support systems;
Medical image analysis for computer-aided diagnosis;
Large language models in health informatics;
Time-series analysis for disease progression modeling;
Multimodal fusion for disease detection and treatment;
Medical AI for wearable and pervasive sensing;
Digital twin and cognitive AI;
AI tools for healthcare management;
Disease biomarker identification for systemic conditions;
Biomedical Generative AI;
Personalized medicine and treatment response prediction;
Integration of multi-omics data using AI for disease characterization.

Dr. Hisham Daoud
Guest Editor

Manuscript Submission Information

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Keywords

computer-aided diagnostics and treatment
deep learning
machine learning
medical image analysis
health informatics
personalized medicine
disease prediction
disease diagnosis
healthcare management

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Published Papers (7 papers)

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Research

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25 pages, 3236 KB

Open AccessArticle

A Wearable IoT-Based Measurement System for Real-Time Cardiovascular Risk Prediction Using Heart Rate Variability

by Nurdaulet Tasmurzayev, Bibars Amangeldy, Timur Imankulov, Baglan Imanbek, Octavian Adrian Postolache and Akzhan Konysbekova

Eng 2025, 6(10), 259; https://doi.org/10.3390/eng6100259 - 2 Oct 2025

Viewed by 787

Abstract

Cardiovascular diseases (CVDs) remain the leading cause of global mortality, with ischemic heart disease (IHD) being the most prevalent and deadly subtype. The growing burden of IHD underscores the urgent need for effective early detection methods that are scalable and non-invasive. Heart Rate Variability (HRV), a non-invasive physiological marker influenced by the autonomic nervous system (ANS), has shown clinical relevance in predicting adverse cardiac events. This study presents a photoplethysmography (PPG)-based Zhurek IoT device, a custom-developed Internet of Things (IoT) device for non-invasive HRV monitoring. The platform’s effectiveness was evaluated using HRV metrics from electrocardiography (ECG) and PPG signals, with machine learning (ML) models applied to the task of early IHD risk detection. ML classifiers were trained on HRV features, and the Random Forest (RF) model achieved the highest classification accuracy of 90.82%, precision of 92.11%, and recall of 91.00% when tested on real data. The model demonstrated excellent discriminative ability with an area under the ROC curve (AUC) of 0.98, reaching a sensitivity of 88% and specificity of 100% at its optimal threshold. The preliminary results suggest that data collected with the “Zhurek” IoT devices are promising for the further development of ML models for IHD risk detection. This study aimed to address the limitations of previous work, such as small datasets and a lack of validation, by utilizing real and synthetically augmented data (conditional tabular GAN (CTGAN)), as well as multi-sensor input (ECG and PPG). The findings of this pilot study can serve as a starting point for developing scalable, remote, and cost-effective screening systems. The further integration of wearable devices and intelligent algorithms is a promising direction for improving routine monitoring and advancing preventative cardiology. Full article

(This article belongs to the Special Issue Advanced Artificial Intelligence Techniques for Disease Prediction, Diagnosis and Management)

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21 pages, 1689 KB

Open AccessArticle

Exploring LLM Embedding Potential for Dementia Detection Using Audio Transcripts

by Brandon Alejandro Llaca-Sánchez, Luis Roberto García-Noguez, Marco Antonio Aceves-Fernández, Andras Takacs and Saúl Tovar-Arriaga

Eng 2025, 6(7), 163; https://doi.org/10.3390/eng6070163 - 17 Jul 2025

Cited by 1 | Viewed by 1022

Abstract

Dementia is a neurodegenerative disorder characterized by progressive cognitive impairment that significantly affects daily living. Early detection of Alzheimer’s disease—the most common form of dementia—remains essential for prompt intervention and treatment, yet clinical diagnosis often requires extensive and resource-intensive procedures. This article explores the effectiveness of automated Natural Language Processing (NLP) methods for identifying Alzheimer’s indicators from audio transcriptions of the Cookie Theft picture description task in the PittCorpus dementia database. Five NLP approaches were compared: a classical Tf–Idf statistical representation and embeddings derived from large language models (GloVe, BERT, Gemma-2B, and Linq-Embed-Mistral), each integrated with a logistic regression classifier. Transcriptions were carefully preprocessed to preserve linguistically relevant features such as repetitions, self-corrections, and pauses. To compare the performance of the five approaches, a stratified 5-fold cross-validation was conducted; the best results were obtained with BERT embeddings (84.73% accuracy) closely followed by the simpler Tf–Idf approach (83.73% accuracy) and the state-of-the-art model Linq-Embed-Mistral (83.54% accuracy), while Gemma-2B and GloVe embeddings yielded slightly lower performances (80.91% and 78.11% accuracy, respectively). Contrary to initial expectations—that richer semantic and contextual embeddings would substantially outperform simpler frequency-based methods—the competitive accuracy of Tf–Idf suggests that the choice and frequency of the words used might be more important than semantic or contextual information in Alzheimer’s detection. This work represents an effort toward implementing user-friendly software capable of offering an initial indicator of Alzheimer’s risk, potentially reducing the need for an in-person clinical visit. Full article

(This article belongs to the Special Issue Advanced Artificial Intelligence Techniques for Disease Prediction, Diagnosis and Management)

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18 pages, 797 KB

Open AccessArticle

A Two-Level Rule-Mining Approach to Classify Breast Cancer Patterns Using Adaptive Directed Mutation and Genetic Algorithm

by Hui-Ching Wu and Ming-Hseng Tseng

Eng 2025, 6(7), 154; https://doi.org/10.3390/eng6070154 - 7 Jul 2025

Viewed by 479

Abstract

Breast cancer represents a significant public health concern in both Western countries and Asia. Accurate and early detection is critical to improving long-term patient survival. For physicians to understand the classification and decision rules and to evaluate their results, it is preferable to use white box approaches to develop prediction models. This paper proposes a novel classification technique for extracting malignant prediction rules from training datasets containing numerical and binary nominal attributes. The classification technique introduced in this study facilitates the discovery of breast cancer patterns by integrating a real-coded genetic algorithm, an adaptive directed mutation operator, and a two-level malignant-rule-mining process. The experimental results, compared with existing rule-based methods from previous studies, demonstrate that the proposed approach generates simple and interpretable decision rules and effectively identifies patterns that lead to accurate breast cancer classification. Full article

(This article belongs to the Special Issue Advanced Artificial Intelligence Techniques for Disease Prediction, Diagnosis and Management)

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20 pages, 2132 KB

Open AccessArticle

Deep Learning with Dual-Channel Feature Fusion for Epileptic EEG Signal Classification

by Bingbing Yu, Mingliang Zuo and Li Sui

Eng 2025, 6(7), 150; https://doi.org/10.3390/eng6070150 - 2 Jul 2025

Viewed by 838

Abstract

Background: Electroencephalography (EEG) signals play a crucial role in diagnosing epilepsy by reflecting distinct patterns associated with normal brain activity, ictal (seizure) states, and interictal (between-seizure) periods. However, the manual classification of these patterns is labor-intensive, time-consuming, and depends heavily on specialized expertise. While deep learning methods have shown promise, many current models suffer from limitations such as excessive complexity, high computational demands, and insufficient generalizability. Developing lightweight and accurate models for real-time epilepsy detection remains a key challenge. Methods: This study proposes a novel dual-channel deep learning model to classify epileptic EEG signals into three categories: normal, ictal, and interictal states. Channel 1 integrates a bidirectional long short-term memory (BiLSTM) network with a Squeeze-and-Excitation (SE) ResNet attention module to dynamically emphasize critical feature channels. Channel 2 employs a dual-branch convolutional neural network (CNN) to extract deeper and distinct features. The model’s performance was evaluated on the publicly available Bonn EEG dataset. Results: The proposed model achieved an outstanding accuracy of 98.57%. The dual-channel structure improved specificity to 99.43%, while the dual-branch CNN boosted sensitivity by 5.12%. Components such as SE-ResNet attention modules contributed 4.29% to the accuracy improvement, and BiLSTM further enhanced specificity by 1.62%. Ablation studies validated the significance of each module. Conclusions: By leveraging a lightweight design and attention-based mechanisms, the dual-channel model offers high diagnostic precision while maintaining computational efficiency. Its applicability to real-time automated diagnosis positions it as a promising tool for clinical deployment across diverse patient populations. Full article

(This article belongs to the Special Issue Advanced Artificial Intelligence Techniques for Disease Prediction, Diagnosis and Management)

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Review

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24 pages, 1024 KB

Open AccessReview

Artificial Intelligence in Glioma Diagnosis: A Narrative Review of Radiomics and Deep Learning for Tumor Classification and Molecular Profiling Across Positron Emission Tomography and Magnetic Resonance Imaging

by Rafail C. Christodoulou, Rafael Pitsillos, Platon S. Papageorgiou, Vasileia Petrou, Georgios Vamvouras, Ludwing Rivera, Sokratis G. Papageorgiou, Elena E. Solomou and Michalis F. Georgiou

Eng 2025, 6(10), 262; https://doi.org/10.3390/eng6100262 - 3 Oct 2025

Viewed by 749

Abstract

Background: This narrative review summarizes recent progress in artificial intelligence (AI), especially radiomics and deep learning, for non-invasive diagnosis and molecular profiling of gliomas. Methodology: A thorough literature search was conducted on PubMed, Scopus, and Embase for studies published from January 2020 to July 2025, focusing on clinical and technical research. In key areas, these studies examine AI models’ predictive capabilities with multi-parametric Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET). Results: The domains identified in the literature include the advancement of radiomic models for tumor grading and biomarker prediction, such as Isocitrate Dehydrogenase (IDH) mutation, O6-methylguanine-dna methyltransferase (MGMT) promoter methylation, and 1p/19q codeletion. The growing use of convolutional neural networks (CNNs) and generative adversarial networks (GANs) in tumor segmentation, classification, and prognosis was also a significant topic discussed in the literature. Deep learning (DL) methods are evaluated against traditional radiomics regarding feature extraction, scalability, and robustness to imaging protocol differences across institutions. Conclusions: This review analyzes emerging efforts to combine clinical, imaging, and histology data within hybrid or transformer-based AI systems to enhance diagnostic accuracy. Significant findings include the application of DL to predict cyclin-dependent kinase inhibitor 2A/B (CDKN2A/B) deletion and chemokine CCL2 expression. These highlight the expanding capabilities of imaging-based genomic inference and the importance of clinical data in multimodal fusion. Challenges such as data harmonization, model interpretability, and external validation still need to be addressed. Full article

(This article belongs to the Special Issue Advanced Artificial Intelligence Techniques for Disease Prediction, Diagnosis and Management)

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34 pages, 3510 KB

Open AccessReview

Advancing Brain Tumor Analysis: Current Trends, Key Challenges, and Perspectives in Deep Learning-Based Brain MRI Tumor Diagnosis

by Namya Musthafa, Qurban A. Memon and Mohammad M. Masud

Eng 2025, 6(5), 82; https://doi.org/10.3390/eng6050082 - 22 Apr 2025

Cited by 5 | Viewed by 6906

Abstract

Brain tumors pose a significant challenge in medical research due to their associated morbidity and mortality. Magnetic Resonance Imaging (MRI) is the premier imaging technique for analyzing these tumors without invasive procedures. Recent years have witnessed remarkable progress in brain tumor detection, classification, and progression analysis using MRI data, largely fueled by advancements in deep learning (DL) models and the growing availability of comprehensive datasets. This article investigates the cutting-edge DL models applied to MRI data for brain tumor diagnosis and prognosis. The study also analyzes experimental results from the past two decades along with technical challenges encountered. The developed datasets for diagnosis and prognosis, efforts behind the regulatory framework, inconsistencies in benchmarking, and clinical translation are also highlighted. Finally, this article identifies long-term research trends and several promising avenues for future research in this critical area. Full article

(This article belongs to the Special Issue Advanced Artificial Intelligence Techniques for Disease Prediction, Diagnosis and Management)

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Other

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30 pages, 1153 KB

Open AccessSystematic Review

Machine Learning-Based Approaches for Early Detection and Risk Stratification of Deep Vein Thrombosis: A Systematic Review

by Andre Axel Cadena Zepeda, Enrique Efrén García-Guerrero, Oscar Adrian Aguirre-Castro, Gilberto Manuel Galindo-Aldana, Reyes Juárez-Ramírez, Marco Antonio Gómez-Guzmán, Christian Raymond and Everardo Inzunza-Gonzalez

Eng 2025, 6(9), 243; https://doi.org/10.3390/eng6090243 - 14 Sep 2025

Viewed by 719

Abstract

Deep vein thrombosis is a condition associated with substantial morbidity and a high risk of pulmonary embolism, underscoring the need for rapid and reliable diagnostic solutions. Although machine learning and deep learning techniques are increasingly being applied for clinical decision support, comprehensive analyses of their contributions to early detection, risk prediction, and monitoring remain limited. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines, we conducted a systematic search in ScienceDirect, IEEE Xplore, Scopus, and Web of Science for studies published between January 2014 and March 2025. Eligible studies applied machine learning or deep learning approaches for the early prediction, monitoring, or risk assessment of deep vein thrombosis, or described reference datasets for algorithm development. Two authors independently extracted data and evaluated methodological quality using the Quality Assessment of Diagnostic Accuracy Studies-2 framework. The included studies were categorized into four domains: Early prediction, monitoring, risk assessment, and reference datasets. In total, 66 studies met the inclusion criteria. Recent advances include deep learning-assisted ultrasound interpretation and real-time implementation of machine learning algorithms. While most studies demonstrated a low overall risk of bias, recurring limitations were identified in terms of patient selection, reporting practices, and validation strategies. Dataset harmonization and external validation were infrequently performed, and documentation of data provenance and class imbalance handling was inconsistent. Machine learning and deep learning approaches demonstrate considerable potential to accelerate accurate diagnoses and facilitate individualized risk stratification; however, their translation into routine practice requires standardized datasets, rigorous external validation, and integration into existing clinical workflows. This review consolidates a decade of research, links methodological quality to clinical applicability, and provides a task-oriented roadmap for advancing machine learning-enabled diagnostics and monitoring in the context of deep vein thrombosis. Full article

(This article belongs to the Special Issue Advanced Artificial Intelligence Techniques for Disease Prediction, Diagnosis and Management)

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