Artificial Intelligence in Ocular Surface Oncology: A Narrative Review of Multimodal Imaging, Smartphone-Based Screening, and Clinical Decision Support
8 affiliations
- 1Department of Medicine and Surgery, University of Enna “Kore”, Piazza dell’Università, 94100 Enna, Italy
- 2Mediterranean Foundation “G.B. Morgagni”, 95125 Catania, Italy
- 3Department of Ophthalmology, University Hospital of Udine, 33100 Udine, Italy
- 4Department of Medicine, Surgery and Health Sciences, University of Trieste, 34129 Trieste, Italy
- 5Department of Ophthalmology, University of Catania, 95123 Catania, Italy
- 6Department of Ophthalmology, Ospedale Sant’Antonio, Azienda Ospedaliera, 35127 Padova, Italy
- 7Imperial College Ophthalmic Research Group (ICORG) Unit, Imperial College, 153-173 Marylebone Rd., London NW1 5QH, UK
- 8Eye Clinic, Catania University, Policlinico G. Rodolico, 95121 Catania, Italy
Abstract
Ocular surface malignancies include ocular surface squamous neoplasia (OSSN), con-junctival melanoma, and conjunctival lymphoma. These rare disorders may threaten vi-sion and, in some cases, life. Early identification may be difficult because malignant le-sions can resemble benign ocular surface conditions. Artificial intelligence (AI), including machine learning and deep learning, has emerged as a potential aid to detection, classi-fication, and referral. This structured narrative review, organized with reference to SANRA principles, synthesizes PubMed/MEDLINE literature on AI applications in ocular surface oncology available through 29 August 2026. We examine slit-lamp photography, anterior segment optical coherence tomography, in vivo confocal microscopy, ultrasound biomicroscopy, and smartphone imaging. Deep-learning models have shown promising performance in selected binary OSSN tasks, whereas multiclass studies report lower sen-sitivity for rare malignancies such as conjunctival melanoma and lymphoma. Smartphone-based screening has also been evaluated in a prospective referral pathway, although the reference standard and verification process limit interpretation of popula-tion-level sensitivity. Dataset imbalance, image-acquisition variability, limited external and prospective validation, explainability, and workflow integration remain barriers to implementation. Future work should prioritize multicenter datasets, standardized acquisition, calibrated thresholds, and prospective human–AI evaluations. Current evidence supports further evaluation of AI as an adjunct for detection and referral, not as a re-placement for specialist assessment or histopathology.
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BibTeX
@article{cappellani2026artificial,
title = {{Artificial Intelligence in Ocular Surface Oncology: A Narrative Review of Multimodal Imaging, Smartphone-Based Screening, and Clinical Decision Support}},
author = {Francesco Cappellani and Marco Zeppieri and Antonio Longo and Cosimo Mazzotta and Irene Gattazzo and Fabiana D’Esposito and Caterina Gagliano and Matteo Capobianco},
journal = {Journal of Clinical Medicine},
year = {2026},
month = oct,
doi = {10.3390/jcm15207785},
url = {https://www.mdpi.com/2077-0383/15/20/7785},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Artificial Intelligence in Ocular Surface Oncology: A Narrative Review of Multimodal Imaging, Smartphone-Based Screening, and Clinical Decision Support
AU - Francesco Cappellani
AU - Marco Zeppieri
AU - Antonio Longo
AU - Cosimo Mazzotta
AU - Irene Gattazzo
AU - Fabiana D’Esposito
AU - Caterina Gagliano
AU - Matteo Capobianco
PY - 2026
DA - 2026/10/09
JO - Journal of Clinical Medicine
DO - 10.3390/jcm15207785
UR - https://www.mdpi.com/2077-0383/15/20/7785
AB - Ocular surface malignancies include ocular surface squamous neoplasia (OSSN), con-junctival melanoma, and conjunctival lymphoma. These rare disorders may threaten vi-sion and, in some cases, life. Early identification may be difficult because malignant le-sions can resemble benign ocular surface conditions. Artificial intelligence (AI), including machine learning and deep learning, has emerged as a potential aid to detection, classi-fication, and referral. This structured narrative review, organized with reference to SANRA principles, synthesizes PubMed/MEDLINE literature on AI applications in ocular surface oncology available through 29 August 2026. We examine slit-lamp photography, anterior segment optical coherence tomography, in vivo confocal microscopy, ultrasound biomicroscopy, and smartphone imaging. Deep-learning models have shown promising performance in selected binary OSSN tasks, whereas multiclass studies report lower sen-sitivity for rare malignancies such as conjunctival melanoma and lymphoma. Smartphone-based screening has also been evaluated in a prospective referral pathway, although the reference standard and verification process limit interpretation of popula-tion-level sensitivity. Dataset imbalance, image-acquisition variability, limited external and prospective validation, explainability, and workflow integration remain barriers to implementation. Future work should prioritize multicenter datasets, standardized acquisition, calibrated thresholds, and prospective human–AI evaluations. Current evidence supports further evaluation of AI as an adjunct for detection and referral, not as a re-placement for specialist assessment or histopathology.
SN - 2077-0383
ER -