ArticleCell reports. Medicine2025
Ultra-wide-field fundus photography and AI-based screening and referral for multiple ocular fundus diseases.
Article in Cell reports. Medicine, 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.
- Macular hole detection and segmentation on fundus photography using large multimodal generative models for synthetic augmentation.BMC ophthalmology · 2026Article
- Ultra-widefield color fundus photography in diabetic retinopathy: from panretinal assessment to multimodal integration.Frontiers in medicine · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
37 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To address the difficulty in comprehensive screening of fundus diseases, we develop three deep learning algorithms (DLAs) based on different algorithms (Swin Transformer and cross-domain collaborative learning [CdCL]) and imaging modalities (ultra-wide-field [UWF] images and the cropped posterior-pole-region [PPR] images) to identify 25 fundus conditions and provide referral suggestions: WARM (CdCL + UWF images), BASE (Swin Transformer + UWF images), and WARM-PPR (CdCL + PPR images). 59,475 UWF images are included to establish internal and external datasets. WARM shows the best performance on the internal test (area under the receiver operating characteristic curve [AUC] for screening = 0.915; AUC for referral = 0.911) and the external multi-center test (AUC for screening = 0.912; AUC for referral = 0.902). UWF images and the CdCL approach significantly enhance the DLA's ability to detect abnormalities in the peripheral retina. The WARM model shows promise as a reliable and accurate tool for comprehensive fundus screening on a large scale.
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.