Artificial Intelligence in Eye Disease, 4th 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 March 2026 | Viewed by 672
Special Issue Editor
2. Department of Artificial Intelligence (Secondary), Korea University, Seoul 136-701, Republic of Korea
Interests: artificial intelligence in biomedicine; diagnosis of retinal diseases; deep learning for ophthalmology images; neuroscience research
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
While the use of artificial intelligence (AI) is rapidly spreading to the medical world amid the vortex of the Fourth Industrial Revolution, the use of AI in ophthalmology is attracting attention for the diagnosis of various ophthalmic diseases, including optic nerve diseases, which are difficult to diagnose. In particular, introducing AI could help to make diagnoses with high accuracy when applied to fundus photographs, optical coherence tomography, and the visual field in order to achieve a strong classification performance in the detection of ocular and retinal diseases. In ocular imaging, AI can be used as a possible solution for screening, diagnosing, and monitoring patients with major eye diseases in primary care and community settings. For instance, through deep learning algorithms that read retinal images, various diseases can be observed, such as bleeding, macular abnormalities—e.g., drusen—choroidal abnormalities, retinal vessel abnormalities, nerve fiber layer defects, and glaucomatous optic nerve papilla changes. Thus, deep learning architectures can be applied to learn to recognize eye diseases, thereby increasing the diagnosis rate with a clinically acceptable performance. In other words, AI serves as a safety device for both patients and doctors, as well as an auxiliary tool to quickly judge the results. It prevents the possibility of an initial misdiagnosis, provides treatment efficiency, and increases patient reliability. Consequently, AI could potentially revolutionize the way that ophthalmology is practiced in the future. Thus, the aim of this Special Issue is to highlight the recent progress and trends in utilizing AI techniques, such as machine learning and deep learning, for detecting, screening, diagnosing, and monitoring numerous eye diseases, not only in diverse clinical practice but also in basic research on ophthalmology.
Prof. Dr. Jae-Ho Han
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
- medical diagnosis
- artificial intelligence
- deep learning
- fundus image
- optical coherence tomography
- ophthalmology
- retinal vessel
- glaucoma
- retinopathy
- macular degeneration
- image segmentation
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