Special Issues
Table of Content

Expanding Horizons in Ophthalmic Diagnostics: A Multidisciplinary AI Approach

Submission Deadline: 31 December 2024 (closed) View: 833

Guest Editors

Dr. Anas Bilal, Hainan Normal University, China
Dr. Sahraoui Dhelim, University College Dublin, Ireland
Dr. Abdulkareem Alzahrani, Al-Baha University, Saudi Arabia

Summary

The integration of artificial intelligence (AI) into ophthalmology has opened up unprecedented opportunities for diagnosing and managing eye diseases. This special issue, titled "Expanding Horizons in Ophthalmic Diagnostics: A Multidisciplinary AI Approach," seeks to showcase innovative AI-driven methodologies that are shaping the future of ophthalmic diagnostics. Our focus will extend across a broad spectrum of ophthalmic diseases, leveraging a variety of AI technologies.


This special issue aims to unite researchers, practitioners, and experts in computer science and medicine to explore innovative approaches to diagnosing eye conditions, with a particular focus on retinal disorders. We seek to integrate deep learning algorithms and nature-inspired optimization techniques to enhance the accuracy and efficiency of diagnostics across a broad spectrum of eye diseases. Contributions that investigate the use of advanced computational models to improve diagnostic precision and patient outcomes are highly encouraged. This includes, but is not limited to, studies on retinal disorders.


Potential Research Topics:

1.Advanced AI Techniques for Eye Disease Detection: Exploration of AI in detecting common and rare ophthalmic conditions, such as glaucoma, cataract, diabetic retinopathy, and macular degeneration.

2.Algorithmic Innovation in Ophthalmology: Application of hybrid AI models and innovative optimization techniques to enhance image analysis and diagnostic precision.

3.Transparent AI Systems: Development of explainable AI systems that provide clear diagnostic reasoning to support clinical decision-making.

4.Comprehensive Diagnostic Frameworks: Utilization of clinical data with imaging to develop robust, multimodal diagnostic systems.

5.Practical Implementations and Case Studies: Insights into the deployment of AI technologies in clinical settings, focusing on challenges, successes, and patient outcomes.

6.Ethical and Regulatory Aspects of AI in Medicine: Exploration of the ethical, privacy, and regulatory considerations when implementing AI in clinical practice.


We encourage researchers and practitioners to participate in this special issue to foster collaboration and advance the state-of-the-art in retinal disorder diagnosis. Your contributions will play a vital role in developing innovative and effective diagnostic tools with the potential to improve patient outcomes in ophthalmic healthcare.


Keywords

Artificial Intelligence
Ophthalmology
Diabetic retinopathy
Cataract
Glaucoma
Deep learning
Machine Learning
Nature Inspired Algorithms
Medical imaging

Published Papers


  • Open Access

    ARTICLE

    MVLA-Net: A Multi-View Lesion Attention Network for Advanced Diagnosis and Grading of Diabetic Retinopathy

    Tariq Mahmood, Tanzila Saba, Faten S. Alamri, Alishba Tahir, Noor Ayesha
    CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1173-1193, 2025, DOI:10.32604/cmc.2025.061150
    (This article belongs to the Special Issue: Expanding Horizons in Ophthalmic Diagnostics: A Multidisciplinary AI Approach)
    Abstract Innovation in learning algorithms has made retinal vessel segmentation and automatic grading techniques crucial for clinical diagnosis and prevention of diabetic retinopathy. The traditional methods struggle with accuracy and reliability due to multi-scale variations in retinal blood vessels and the complex pathological relationship in fundus images associated with diabetic retinopathy. While the single-modal diabetic retinopathy grading network addresses class imbalance challenges and lesion representation in fundus image data, dual-modal diabetic retinopathy grading methods offer superior performance. However, the scarcity of dual-modal data and the lack of effective feature fusion methods limit their potential due to… More >

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