ArticleFrontiers in medicine2026
Artificial intelligence framework for multi-pathology risk assessment from retinal fundus images: deep learning approach to 15-disease screening.
Article in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Automated disease screening systems face challenges when applied to multi-class medical image analysis, particularly under severe class imbalance inherent in clinical datasets. Retinal fundus imaging enables non-invasive screening for multiple ocular and systemic diseases simultaneously, yet current automated systems typically assess risk for only a single pathology or a limited disease range. We developed a comprehensive AI framework for simultaneous risk stratification of 15 distinct pathological conditions from retinal fundus images and report its preliminary evaluation in a real-world clinical setting. Methods: Our system combines fundus images from publicly available sources and proprietary clinical archives, addressing the significant class imbalance challenge inherent in rare condition risk assessment. We employed a hybrid deep learning architecture (CAFormer B36) with focal loss and targeted oversampling to ensure robust performance across common and rare conditions. The framework identifies retinal biomarkers associated with both primary ocular diseases (diabetic retinopathy, glaucoma, macular degeneration, cataracts, retinitis pigmentosa) and systemic conditions with retinal manifestations (hypertensive retinopathy, atherosclerotic changes, autoimmune manifestations). Dataset splitting was performed at the image level; all internal metrics should therefore be interpreted as exploratory upper-bound estimates pending patient-level replication. Results: On the internal image-level test set, the system achieved ROC AUC of 0.9524-0.9971 across all 15 pathological classes, with F1 scores of 0.8968-0.9649. Notably, rare conditions with fewer than 100 training examples demonstrated robust risk stratification performance. In an exploratory single-site evaluation on 68 real-world cases, overall accuracy was 64.7% (95% CI: 52.9-76.5%). Due to the very limited number of sight-threatening cases in this small cohort (glaucoma Conclusions: This pilot proof-of-concept demonstrates the feasibility of multi-pathology risk stratification under severe class imbalance using a hybrid deep learning architecture. Internal image-level metrics show strong discriminative capability, while the preliminary single-site evaluation (
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.