ArticleFrontiers in cell and developmental biology2025
Diagnostic performance and generalizability of deep learning for multiple retinal diseases using bimodal imaging of fundus photography and optical coherence tomography.
Article in Frontiers in cell and developmental biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. 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
- Erratum issued
Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: To develop and evaluate deep learning (DL) models for detecting multiple retinal diseases using bimodal imaging of color fundus photography (CFP) and optical coherence tomography (OCT), assessing diagnostic performance and generalizability. Methods: This cross-sectional study utilized 1445 CFP-OCT pairs from 1,029 patients across three hospitals. Five bimodal models developed, and the model with best performance (Fusion-MIL) was tested and compared with CFP-MIL and OCT-MIL. Models were trained on 710 pairs (Maestro device), validated on 241, and tested on 255 (dataset 1). Additional tests used different devices and scanning patterns: 88 pairs (dataset 2, DRI-OCT), 91 (dataset 3, DRI-OCT), 60 (dataset 4, Visucam/VG200 OCT). Seven retinal conditions, including normal, diabetic retinopathy, dry and wet age-related macular degeneration, pathologic myopia (PM), epiretinal membran, and macular edema, were assessed. PM ATN (atrophy, traction, neovascularization) classification was trained and tested on another 1,184 pairs. Area under receiver operating characteristic curve (AUC) was calculated to evaluated the performance. Results: Fusion-MIL achieved mean AUC 0.985 (95% CI 0.971-0.999) in dataset 2, outperforming CFP-MIL (0.876, Conclusion: Bimodal Fusion-MIL improved diagnosis over single-modal models, showing strong generalizability across devices and detailed grading ability, valuable for various scenarios.
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.