ArticleChinese medical journal2026
Merging artificial intelligence and retinal vascular geometric parameters: A novel tool for diagnosis and prognosis prediction of diabetic nephropathy.
Article in Chinese medical journal, 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
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDiabetic nephropathy (DN) is the leading cause of end-stage renal disease. The retinal microvasculature, as the only directly observable microvasculature, may reflect DN progression. This study aims to construct a non-invasive diagnostic and prognostic prediction model using the mixed effects of retinal vascular geometric parameters and clinical data.
methodsWe constructed a multimodal database including 397 patients with type 2 diabetes and chronic kidney disease from multiple centers in China. The primary cohort (374 patients) was recruited from the Department of Nephrology at the First Medical Center of the Chinese People's Liberation Army General Hospital in Beijing between 2017 and 2022, while an external validation cohort (23 patients) was collected from five other hospitals across China from September 2022 to March 2023. Fundus images, clinical characteristics, renal biopsy diagnoses, and follow-up data were collected. Unsupervised learning and Resnet neural networks were used to segment and calculate retinal vascular geometric parameters. Weighted quantile regression (WQS), Lasso, and COX univariable regressions were employed to assess the mixed effects of retinal vascular geometric parameters and select relevant clinical characteristics. Logistic regression and Cox regression with random forest (COX-RF) were used for model construction.
resultsA multimodal database of 397 patients was constructed. A diagnostic model combining retinal parameters (WQS-diagnosis) and seven clinical characteristics achieved superior performance: AUC 0.98, accuracy 0.92 on the test set, outperforming models using only clinical data (AUC 0.93) or two retinal parameters plus clinical data (AUC 0.94). On the multi-centre validation set, the model maintained accuracy 0.91 and AUC 0.95. For prognosis, a mixed-effects parameter (WQS-prognosis) was derived; the COX-RF model achieved an AUC of 0.88.
conclusionsRetinal microvasculature are effective biomarkers for DN. The proposed non-invasive model demonstrated high accuracy and generalizability, offering a valuable tool for optimising DN management.
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.