ArticleTranslational vision science & technology2025
Challenges for Opticians in Evaluating Small Pigmented Choroidal Lesions: Potential Support From the MelAInoma Deep Learning Algorithm.
Article in Translational vision science & technology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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
3 citing papers in PubMed, 1 synthesis or guideline pooled 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
- 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
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: To evaluate the diagnostic accuracy of Swedish opticians/optometrists when triaging small pigmented choroidal lesions and determine whether the MelAInoma deep learning algorithm improves referral decisions. Methods: Twenty-nine opticians/optometrists graded 25 fundus photographs (5 melanomas, 20 nevi) with the Mushroom shape, Orange pigment, Large size, Enlargement, Subretinal fluid (MOLES) system and recorded referrals; 25 then used MelAInoma. Predefined referral thresholds were MOLES ≥1 or ≥3 and MelAInoma >63. Diagnostic test statistics-including sensitivity, specificity, positive (PPV) and negative (NPV) predictive values, accuracy, and standard deviation (SD)-plus odds of correct referral and decision curve net benefit were computed. Results: With MOLES ≥1, mean ± SD sensitivity was 98% ± 6%, specificity was 17% ± 16%, PPV was 23%, NPV was 99%, and accuracy was 33%. Raising the cutoff to MOLES ≥3 lowered sensitivity to 75% ± 29% but increased specificity to 53% ± 28%, PPV to 34%, NPV to 93%, and accuracy to 57%. MelAInoma achieved 80% sensitivity, 90% specificity, PPV 67%, NPV 95%, and 88% accuracy. Algorithm guidance quadrupled the odds of correctly referring a melanoma, reduced false-positive referrals 10-fold, and provided net clinical benefit to unaided triage across all relevant threshold probabilities. Conclusions: Although the findings are based on a small image set, including only five melanomas, which limits generalizability, the results suggest that opticians and optometrists detect small choroidal melanomas with high sensitivity but limited specificity. Incorporating MelAInoma at the predefined threshold reduced sensitivity, substantially improved specificity, and markedly reduced unnecessary referrals. Translational Relevance: MelAInoma offers practical artificial intelligence support that can streamline community referrals and facilitate earlier treatment of uveal melanoma.
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.