ArticleOphthalmology science2026
What Lies beneath Diabetic Macular Edema: Latent Phenotypic Clustering and Differential Treatment Responses to Intravitreal Therapies.
Article in Ophthalmology science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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.
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Who cites it
3 citing papers in PubMed.
- Hard Exudates in Diabetic Macular Edema: Automated OCT Quantification and Longitudinal Response to Faricimab.Ophthalmology science · 2026Article
- Scan Density Matters: Reproducibility of AI-Derived OCT Biomarkers in Diabetic Macular Edema.Translational vision science & technology · 2026Article
- Anatomical and Systemic Predictors of Early Response to Subthreshold Micropulse Laser in Diabetic Macular Edema: A Retrospective Cohort Study.Journal of clinical medicine · 2026Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: To identify latent phenotypic subgroups of diabetic macular edema (DME) using artificial intelligence-based OCT metrics and evaluate whether treatment responses to anti-VEGF and dexamethasone (DEX) therapies differ across these phenotypic clusters. Methods: Retrospective study including 114 eyes (82 patients) with treatment-naïve DME. Quantitative OCT metrics, including intraretinal fluid (IRF) and subretinal fluid volumes, IRF % distribution within central 0-1, 1-3, and 3-6 mm, hyperreflective foci counts, and ellipsoid zone (EZ) % disruption, were analyzed before and after treatment. Main Outcome Measures: Gaussian finite mixture modeling was used to identify distinct DME subgroups. Changes in visual acuity (VA) and OCT parameters following anti-VEGF or DEX therapy were analyzed using linear and generalized linear mixed-effects models, with false discovery rate correction applied to account for multiple comparisons. Results: Three phenotypic clusters of DME were identified, each demonstrating distinct structural and functional characteristics: cluster 1 (29%, 95% confidence interval [CI]: 20.0%-38.4%), characterized by localized central IRF (mean 0.34 mm Conclusions: Latent heterogeneity in DME presentations may influence treatment responses. Artificial intelligence-derived spectral-domain OCT metrics could support tailored therapeutic approaches to optimize patient outcomes. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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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 Socratic graph.