ArticleOphthalmology science
Differentiating Choroidal Melanomas and Nevi Using a Self-Supervised Deep Learning Model Applied to Clinical Fundoscopy Images.
Article in Ophthalmology science. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.
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Who cites it
12 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Deep learning for early diagnosis of uveal melanoma: a systematic review and meta-analysis.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Pooled it
- Artificial Intelligence-Driven Differentiation Between Uveal Melanoma and Nevus Based on Fundus Photographs: A Systematic Review and Meta-Analysis.Translational vision science & technology · 2026Pooled it
- Imaging-based machine learning for the diagnosis and prognosis of uveal melanoma: a systematic review and meta analysis.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Review
- Choroidal Melanocytic Lesion Detection Using Patch Vectors With a Foundational Vision Transformer.Translational vision science & technology · 2026Article
- Review
- Artificial Intelligence in the Detection and Risk Stratification of Choroidal Melanoma: A Critical Comparative Synthesis and Future Directions.Healthcare (Basel, Switzerland) · 2025Review
- Development of a deep learning model to classify choroidal melanoma risk factors based on color fundus photographs.AJO international · 2025Article
- Automated Assessment of Choroidal Mass Dimensions Using Static and Dynamic Ultrasonographic Imaging.Translational vision science & technology · 2025Article
- Artificial Intelligence in Ophthalmology: Practical Applications, Subspecialty Evidence and Real-World Deployment.Cureus · 2025Review
- Clinical Applications of Optical Coherence Tomography and Optical Coherence Tomography Angiography in Uveal Melanoma: A Narrative Review.Diagnostics (Basel, Switzerland) · 2025Review
- Challenges for Opticians in Evaluating Small Pigmented Choroidal Lesions: Potential Support From the MelAInoma Deep Learning Algorithm.Translational vision science & technology · 2025Article
- Artificial intelligence in the diagnosis of uveal melanoma: advances and applications.Experimental biology and medicine (Maywood, N.J.) · 2025Review
Corrections and comments
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Purpose: Testing the validity of a self-supervised deep learning (DL) model, RETFound, for use on posterior uveal (choroidal) melanoma (UM) and nevus differentiation. Design: Case-control study. Subjects: Ultrawidefield fundoscopy images, both color and autofluorescence, were used for this study, obtained from 4255 patients seen at the Liverpool Ocular Oncology Center between 1995 and 2020. Methods: After excluding poor-quality images, a total of 18 510 UM, 8671 nevi, and 1192 healthy eye images were analyzed. RETFound, a self-supervised DL model for fundus images, was fine-tuned initially for binary classification of UM versus nevi and then retuned for tertiary classification including the healthy eyes. Main Outcome Measures: The performance metrics used to evaluate the model were: area under the receiver operating characteristic curve (AUROC), accuracy, specificity, sensitivity, F1-score, and Matthew's correlation coefficient. Results: For the binary classification task, the model achieved an accuracy of 0.83 and an AUROC of 0.90 demonstrating good performance for UM versus nevi differentiation. Similarly, for the tertiary classification task, the model showed a mean accuracy of 0.82 and an AUROC of 0.92. Conclusions: Our findings demonstrate the feasibility of using a self-supervised DL model for differentiation between UM and nevi with high accuracy, in a large cohort with imbalances between images derived from a single center. Validation studies on similarly sized external cohorts are planned to test our model's potential, considering variation of images of choroidal melanoma and nevi in the clinical setting. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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