ArticleOphthalmology science
Development and Validation of a Deep Learning Algorithm for Differentiation of Choroidal Nevi from Small Melanoma in Fundus Photographs.
Article in Ophthalmology science. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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Who cites it
17 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
- Comparison of MOLES and MelCancers · 2026Article
- Attention-Based Multimodal Deep Learning for Uveal Melanoma Classification Using Ultra-Widefield Fundus Images and Ocular Ultrasound.Ophthalmology science · 2026Article
- Artificial Intelligence in Ocular Oncology: Differentiating Choroidal Melanocytic Lesions.Ophthalmology science · 2026Article
- Automatic classification of uveal melanoma response patterns following ruthenium-106 plaque brachytherapy using ultrasound images and deep convolutional neural network.Scientific reports · 2025Article
- Development of a deep learning model to classify choroidal melanoma risk factors based on color fundus photographs.AJO international · 2025Article
- Artificial Intelligence in the Detection and Risk Stratification of Choroidal Melanoma: A Critical Comparative Synthesis and Future Directions.Healthcare (Basel, Switzerland) · 2025Review
- 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
- Enhancing skin lesion classification: a CNN approach with human baseline comparison.PeerJ. Computer science · 2025Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: To develop and validate a deep learning algorithm capable of differentiating small choroidal melanomas from nevi. Design: Retrospective multicenter cohort study. Participants: A total of 802 images from 688 patients diagnosed with choroidal nevi or melanoma. Methods: Wide field and standard field fundus photographs were collected from patients diagnosed with choroidal nevi or melanoma by ocular oncologists during clinical examinations. A lesion was classified as a nevus if it was followed for at least 5 years without being rediagnosed as melanoma. A neural network optimized for image classification was trained and validated on cohorts of 495 and 168 images and subsequently tested on independent sets of 86 and 53 images. Main Outcome Measures: Area under the curve (AUC) in receiver operating characteristic analysis for differentiating small choroidal melanomas from nevi. Results: The algorithm achieved an AUC of 0.88 in both test cohorts, outperforming ophthalmologists using the Mushroom shape, Orange pigment, Large size, Enlargement, and Subretinal fluid (AUC 0.77) and To Find Small Ocular Melanoma Using Helpful Hints Daily (AUC 0.67) risk factors (DeLong's test, Conclusions: This study develops and validates a deep learning algorithm for differentiating small choroidal melanomas from nevi, matching or surpassing the discriminatory performance of experienced human ophthalmologists. Further research will aim to validate its utility in clinical settings. Financial Disclosures: Financial DisclosuresProprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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