ArticleAJO international2025
Development of a deep learning model to classify choroidal melanoma risk factors based on color fundus photographs.
Article in AJO international, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Review
- Comparison of MOLES and MelCancers · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Choroidal melanoma is the most common malignant primary intraocular tumor and can develop either de novo or from a preexisting choroidal nevus, a benign pigmented lesion. Key risk factors for the transformation of choroidal nevus into melanoma include tumor diameter > 5 mm, tumor thickness > 2 mm, orange pigment, subretinal fluid, and low internal reflectivity on ultrasound. However, the assessment of many of these risk factors requires multimodal imaging equipment and skilled subspecialists, only available at tertiary referral centers. In this study, we developed and validated a deep learning approach to identifying these risk factors based solely on fundus images of choroidal nevi. Results indicate acceptable to excellent predictive performance for detection of all five risk factors. These findings suggest that deep learning models may be valuable tools for identifying high-risk choroidal nevi, particularly in resource-limited settings.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.