| ISSN |
1948-9358 (online) |
| Open Access |
This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/ |
| Copyright |
© The Author(s) 2026. Published by Baishideng Publishing Group Inc. All rights reserved. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Ophthalmology |
| Manuscript Type |
Opinion Review |
| Article Title |
From pixels to precision: Artificial intelligence in diabetic eye disease screening and management
|
| Manuscript Source |
Invited Manuscript |
| All Author List |
Francesco Cappellani, Matteo Capobianco, Federico Visalli, Marieme Khouyyi, Mutali Musa, Alessandro Avitabile, Inferrera Leandro, Rosa Giglio, Daniele Tognetto, Caterina Gagliano, Fabiana D’Esposito and Marco Zeppieri |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Marco Zeppieri, Consultant, MD, PhD, Department of Ophthalmology, University Hospital of Udine, Maria Della Misericordia 15, Udine 33100, Italy. mark.zeppieri@asufc.sanita.fvg.it |
| Key Words |
Artificial intelligence; Ophthalmic imaging; Diabetic retinopathy; Deep learning; Macular edema |
| Core Tip |
Artificial intelligence (AI) is transforming diabetic eye care through automated detection, risk assessment, and precision management. Deep learning algorithms utilized in fundus photography and optical coherence tomography imaging now equal or surpass experienced graders in the detection of diabetic retinopathy and macular edema. In addition to screening, AI incorporates multimodal data – such as systemic parameters, imaging biomarkers, and treatment histories – to predict disease progression and therapeutic outcomes. This review articulates contemporary clinical uses, technical underpinnings, and translational obstacles, while delineating potential trajectories for the development of explainable, morally robust, and globally scalable AI systems that can revolutionize the management of diabetic eye disease. |
| Publish Date |
2026-04-14 08:58 |
| Citation |
Cappellani F, Capobianco M, Visalli F, Khouyyi M, Musa M, Avitabile A, Leandro I, Giglio R, Tognetto D, Gagliano C, D’Esposito F, Zeppieri M. From pixels to precision: Artificial intelligence in diabetic eye disease screening and management. World J Diabetes 2026; 17(4): 117094 |
| URL |
https://www.wjgnet.com/1948-9358/full/v17/i4/117094.htm |
| DOI |
https://dx.doi.org/10.4239/wjd.v17.i4.117094 |
