Evidence map›Paper›PMID 42089922›Full record

ArticleOphthalmology and therapy2026

Prognostic Imaging Biomarkers in Diabetic Macular Edema Treated with Anti-VEGF: A Multicenter AI Perspective.

Mariacristina Parravano, Lorenzo Ferro Desideri, Eliana Costanzo, Nina Eldridge, Martin Zinkernagel, Rodrigo Anguita, Riccardo Sacconi, Giuseppe Querques

Abstract read
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Article in Ophthalmology and therapy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

8 authors.

Mariacristina ParravanoUniCamillus-Saint Camillus International University of Health Sciences IT, Via Livenza 3, 00198, Rome, RM, Italy. mcparravano@gmail.com.ORCID http://orcid.org/0000-0002-2223-7311
Lorenzo Ferro DesideriDepartment of Ophthalmology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
Eliana CostanzoIRCCS-Fondazione Bietti, Rome, Italy.ORCID http://orcid.org/0000-0002-9916-8895
Nina EldridgeARTORG Research Center Biomedical Engineering Research, University of Bern, Bern, Switzerland.
Martin ZinkernagelDepartment of Ophthalmology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
Rodrigo AnguitaDepartment of Ophthalmology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
Riccardo SacconiSchool of Medicine, Vita-Salute San Raffaele University, Milan, Italy.
Giuseppe QuerquesDepartment of Surgical, Medical, Dental and Morphological Sciences, Faculty of Medicine, UNIMORE-University of Modena and Reggio Emilia, Modena, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionThis study aimed to identify optical coherence tomography (OCT) biomarkers at baseline and after the loading phase (LP) of antivascular endothelial growth factor (VEGF), predictive of 12 months (12 m) morpho-functional outcomes in diabetic macular edema (DME).

methodsThis multicenter, retrospective study involved treatment-naive DME eyes treated with anti-VEGF agents. The OCT volume scans at baseline, after the LP, and at 12 m were analyzed by an artificial intelligence (AI)-derived platform (Discovery OCT Biomarker Detector; RetinAI AG, Bern, Switzerland). Different retinal layer thicknesses and volumes, intraretinal fluid (IRF), subretinal fluid (SRF), and biomarkers probability detection, including hyperreflective foci (HF) were measured. A random forest model assessed the predictive factors for final morphological and functional outcomes.

resultsA total of 77 treatment-naive DME eyes from 64 patients treated with anti-VEGF (88.3% aflibercept, 11.7% ranibizumab; mean n. of injections 9.93 ± 3.18) were enrolled. A significant reduction of all the retinal layers, IRF, SRF, and retinal volumes (p < 0.05) after the LP and at 12 m was found. The random forest model revealed that a higher baseline IRF volume was a moderate predictor and a lower outer nuclear layer (ONL) thickness after LP was a strong predictor for a good morphological response at 12 m. Best-corrected visual acuity (BCVA) prediction remained limited due to weaker associations with OCT biomarkers.

conclusionsAI-derived software showed promise in detecting OCT biomarkers and improving 1-year outcome prediction in DME management. Baseline IRF volume and ONL thickness after the LP were strong predictors of achieving a structural response at 12 m, with overall good model performance.

Indexed as

AIDMEOCT biomarkersONL thicknessPrognostic biomarkers

Identifiers

PMID42089922
PMCPMC13250024

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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.