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Eng
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Advanced Artificial Intelligence Techniques for Disease Prediction, Diagnosis and Management
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Advanced Artificial Intelligence Techniques for Disease Prediction, Diagnosis and Management

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

Deadline for manuscript submissions: 31 March 2026 | Viewed by 7891

Special Issue Editor

Dr. Hisham Daoud

E-Mail Website
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

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. Eng 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 1400 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

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

Published Papers (5 papers)

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Research

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21 pages, 1689 KB  
Article
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 713
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 [...] Read more.
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  
Article
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 407
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 [...] Read more.
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  
Article
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 658
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. [...] Read more.
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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34 pages, 3510 KB  
Review
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 3 | Viewed by 5257
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, [...] Read more.
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  
Systematic 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 189
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 [...] Read more.
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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