Evidence map›Paper›PMID 41922489›Full record

ArticleScientific reports2026

Artificial intelligence based assessment of treatment response in wet age related macular degeneration using paired OCT angiography.

Mohamed Sherif Morsy, Nandini Avijit Dutta, Elsaid Ibrahim Eldessouky, Mamdouh Mahmoud Kabil, Hamdy Abd El Azim El-Koumy, Nehal Nailesh Mehta, Amr Lotfy Ali, Soumya Jena, Haochen Zhang, Dirk-Uwe Bartsch and 4 more

Abstract read
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In one paragraph

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

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

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

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Mohamed Sherif MorsyViterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California, La Jolla, 92093, USA.
Nandini Avijit DuttaNational Institute of Technology, Tiruchirapalli, India.
Elsaid Ibrahim EldessoukyOphthalmology Department, Faculty of Medicine, Tanta University, Tanta, Egypt.
Mamdouh Mahmoud KabilOphthalmology Department, Faculty of Medicine, Tanta University, Tanta, Egypt.
Hamdy Abd El Azim El-KoumyOphthalmology Department, Faculty of Medicine, Tanta University, Tanta, Egypt.
Nehal Nailesh MehtaViterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California, La Jolla, 92093, USA.
Amr Lotfy AliJacobs Retina Center, 9415 Campus Point Drive, La Jolla, CA, 92093, USA.
Soumya JenaJacobs Retina Center, 9415 Campus Point Drive, La Jolla, CA, 92093, USA.
Haochen ZhangDepartment of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA.
Dirk-Uwe BartschJacobs Retina Center, 9415 Campus Point Drive, La Jolla, CA, 92093, USA.
Lingyun ChengJacobs Retina Center, 9415 Campus Point Drive, La Jolla, CA, 92093, USA.
Cheolhong AnDepartment of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA.
Truong NguyenDepartment of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA.
William R FreemanJacobs Retina Center, 9415 Campus Point Drive, La Jolla, CA, 92093, USA. wrfreeman@health.ucsd.edu.

Funding

NIH grant R01EY033847,UCSD Vision Research Center Core Grant P30EY022589
6 · The paper itself

Abstract

Artificial intelligence (AI) has shown promise in retinal imaging, yet its application to longitudinal optical coherence tomography angiography (OCTA) remains limited. This study developed and evaluated an AI model for classifying treatment response in neovascular age-related macular degeneration (nAMD) using paired OCTA images acquired before and after anti-VEGF therapy. In this retrospective cohort study, paired OCTA en-face images and corresponding OCT B-scans were collected for each treatment course. OCTA image pairs were manually segmented and aligned for AI input, while ground-truth labels (Improved, Unchanged, Worsened) were determined based on structural OCT findings and clinical visual acuity outcomes. After quality exclusion, 1033 OCTA pairs were included and divided into training, validation, and independent testing subsets. Two experienced retina specialists graded all OCTA pairs for comparison. On the test set, the AI model achieved an overall accuracy of 82.08%, with class-specific accuracies of 74.29% (worsened), 81.48% (unchanged), and 88.64% (improved). In contrast, overall human grading accuracy was 61.40%. Human graders were significantly more likely to misclassify treatment response than the AI model for all groups (odds ratio = 2.88; 95% CI 1.68–4.92; p < 0.0001). These findings demonstrate that AI-based paired OCTA analysis can provide a more accurate and objective assessment of treatment response in nAMD.

Indexed as

Artificial IntelligenceTomography, Optical CoherenceWet Macular DegenerationAgedAged, 80 and overAngiogenesis InhibitorsFemaleFluorescein AngiographyHumansMaleRetrospective StudiesTreatment OutcomeVascular Endothelial Growth Factor AVisual AcuityAngiogenesis InhibitorsVascular Endothelial Growth Factor AAnti-VEGF therapyArtificial intelligenceNeovascular age-related macular degenerationOptical coherence tomography angiographyPaired imagingTreatment response

Identifiers

PMID41922489
PMCPMC13183950

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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.