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Volume 9
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10.3390/vision9030071
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Review

Clinical Applications of Artificial Intelligence in Corneal Diseases

by
Omar Nusair
1,†,
Hassan Asadigandomani
2,†,
Hossein Farrokhpour
3,†,
Fatemeh Moosaie
4,
Zahra Bibak-Bejandi
5,
Alireza Razavi
6,
Kimia Daneshvar
6 and
Mohammad Soleimani
1,*
1
Kittner Eye Center, Department of Ophthalmology, University of North Carolina, Chapel Hill, NC 27517, USA
2
Department of Ophthalmology, University of California, San Francisco, CA 94143, USA
3
Translational Ophthalmology Research Center, Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran 1336616351, Iran
4
Universal Scientific Education and Research Network (USERN), Tehran University of Medical Sciences, Tehran 1416634793, Iran
5
Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, IL 60612, USA
6
Eye Research Center, Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran 1336616351, Iran
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Vision 2025, 9(3), 71; https://doi.org/10.3390/vision9030071
Submission received: 10 June 2025 / Revised: 12 August 2025 / Accepted: 13 August 2025 / Published: 18 August 2025
Versions Notes

Abstract

We evaluated the clinical applications of artificial intelligence models in diagnosing corneal diseases, highlighting their performance metrics and clinical potential. A systematic search was conducted for several disease categories: keratoconus (KC), Fuch’s endothelial corneal dystrophy (FECD), infectious keratitis (IK), corneal neuropathy, dry eye disease (DED), and conjunctival diseases. Metrics such as sensitivity, specificity, accuracy, and area under the curve (AUC) were extracted. Across the diseases, convolutional neural networks and other deep learning models frequently achieved or exceeded established diagnostic benchmarks (AUC > 0.90; sensitivity/specificity > 0.85–0.90), with a particularly strong performance for KC and FECD when trained on consistent imaging modalities such as anterior segment optical coherence tomography (AS-OCT). Models for IK and conjunctival diseases showed promise but faced challenges in heterogeneous image quality and limited objective training criteria. DED and tear film models benefited from multimodal data yet lacked direct comparisons with expert clinicians. Despite high diagnostic precision, challenges from heterogeneous data, a lack of standardization in disease definitions, imaging acquisition, and model training remain. The broad implementation of artificial intelligence must address these limitations to improve eye care equity.
Keywords: artificial intelligence; deep learning; machine learning; corneal diseases; keratoconus; infectious keratitis; dry eye disease; tear film; corneal neuropathy; conjunctiva artificial intelligence; deep learning; machine learning; corneal diseases; keratoconus; infectious keratitis; dry eye disease; tear film; corneal neuropathy; conjunctiva

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MDPI and ACS Style

Nusair, O.; Asadigandomani, H.; Farrokhpour, H.; Moosaie, F.; Bibak-Bejandi, Z.; Razavi, A.; Daneshvar, K.; Soleimani, M. Clinical Applications of Artificial Intelligence in Corneal Diseases. Vision 2025, 9, 71. https://doi.org/10.3390/vision9030071

AMA Style

Nusair O, Asadigandomani H, Farrokhpour H, Moosaie F, Bibak-Bejandi Z, Razavi A, Daneshvar K, Soleimani M. Clinical Applications of Artificial Intelligence in Corneal Diseases. Vision. 2025; 9(3):71. https://doi.org/10.3390/vision9030071

Chicago/Turabian Style

Nusair, Omar, Hassan Asadigandomani, Hossein Farrokhpour, Fatemeh Moosaie, Zahra Bibak-Bejandi, Alireza Razavi, Kimia Daneshvar, and Mohammad Soleimani. 2025. "Clinical Applications of Artificial Intelligence in Corneal Diseases" Vision 9, no. 3: 71. https://doi.org/10.3390/vision9030071

APA Style

Nusair, O., Asadigandomani, H., Farrokhpour, H., Moosaie, F., Bibak-Bejandi, Z., Razavi, A., Daneshvar, K., & Soleimani, M. (2025). Clinical Applications of Artificial Intelligence in Corneal Diseases. Vision, 9(3), 71. https://doi.org/10.3390/vision9030071

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