ArticleNPJ digital medicine2025
A multimodal transformer system for noninvasive diabetic nephropathy diagnosis via retinal imaging.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03865914 (Correlation Study Between Clinical Phenotype and Pathology of Type 2 Diabetic Nephropathy), which is not on this map. Cited by 10 papers.
What it found
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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.
Correlation Study Between Clinical Phenotype and Pathology of Type 2 Diabetic Nephropathy
Who cites it
10 citing papers in PubMed.
- Bibliometrics and visualization analysis: Big data research on diabetic kidney disease from 2000 to 2025.Medicine · 2026Article
- Applications of Machine Learning for Early Diagnosis and Prognosis of Chronic Kidney Disease: Current Evidence.Diagnostics (Basel, Switzerland) · 2026Review
- Explainable AI for diabetic retinopathy detection using vision transformers.Scientific reports · 2026Article
- A brief review of some artificial intelligence methods in nephrology.Pediatric nephrology (Berlin, Germany) · 2026Review
- Current and Future Applications of AI-Driven Predictive Modeling and a Proposed Framework for AI-Bioprognostics in Kidney Care.Risk management and healthcare policy · 2026Review
- Artificial Intelligence in Nephrology: From Early Detection to Clinical Management of Kidney Diseases.Bioengineering (Basel, Switzerland) · 2025Review
- Innovative Approach for Diabetic Retinopathy Severity Classification: An AI-Powered Tool using CNN-Transformer Fusion.Journal of biomedical physics & engineering · 2025Article
- Graph-enhanced multimodal fusion of vascular biomarkers and deep features for diabetic retinopathy detection.Frontiers in artificial intelligence · 2025Article
- Study on Predictive Models for Differential Diagnosis of Diabetic Kidney Disease and Non-Diabetic Kidney Disease Based on Clinical and Biochemical Indicators.International journal of medical sciences · 2025Article
- Repeated Kidney Biopsy in Membranoproliferative Glomerulonephritis.Kidney diseases (Basel, Switzerland)Article
Corrections and comments
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Authors and funding
30 authors.
Funding
Abstract
Differentiating between diabetic nephropathy (DN) and non-diabetic renal disease (NDRD) without a kidney biopsy remains a major challenge, often leading to missed opportunities for targeted treatments that could greatly improve NDRD outcomes. To reform the traditional biopsy-all diagnostic paradigm and avoid unnecessary biopsy, we developed a transformer-based deep learning (DL) system for detecting DN and NDRD upon non-invasive multi-modal data of fundus images and clinical characteristics. Our Trans-MUF achieved an AUC of 0.980 (95% CI: 0.979 to 0.980) over the internal retrospective set and also had superior generalizability over a prospective dataset (AUC: 0.989, 95% CI: 0.987 to 0.990) and a multicenter, cross-machine and multi-operator dataset (AUC: 0.932, 95% CI: 0.931 to 0.939). Moreover, the nephrologists' diagnosis accuracy can be improved by 21%, through visualization assistance of the DL system. This paper lays a foundation for automatically differentiating DN and NDRD without biopsy. (Registry name: Correlation Study Between Clinical Phenotype and Pathology of Type 2 Diabetic Nephropathy. ID: NCT03865914. Date: 2017-11-30).
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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.