Evidence map›Paper›PMID 42437118›Full record

ArticleOphthalmology science2026

Quantitative Analysis of Retinal Fluid by a Deep Learning Model in Uveitic Macular Edema.

Anthony Wu, Adrian Au, Justin Hanson, Marcus Yamamoto, Joy Cheng, Simon Lee, Tal Eshkoly Lior, Oren Avram, SriniVas R Sadda, Alison B Coyne and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Ophthalmology science, 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.

Anthony WuJules Stein Eye Institute, University of California, Los Angeles, Los Angeles, California.
Adrian AuJules Stein Eye Institute, University of California, Los Angeles, Los Angeles, California.
Justin HansonJules Stein Eye Institute, University of California, Los Angeles, Los Angeles, California.
Marcus YamamotoJules Stein Eye Institute, University of California, Los Angeles, Los Angeles, California.
Joy ChengDepartment of Computational Medicine, University of California, Los Angeles, Los Angeles, California.
Simon LeeDepartment of Computational Medicine, University of California, Los Angeles, Los Angeles, California.
Tal Eshkoly LiorJules Stein Eye Institute, University of California, Los Angeles, Los Angeles, California.
Oren AvramDepartment of Computational Medicine, University of California, Los Angeles, Los Angeles, California.
SriniVas R SaddaJules Stein Eye Institute, University of California, Los Angeles, Los Angeles, California.
Alison B CoyneFrancis I. Proctor Foundation for Research in Ophthalmology, University of California, San Francisco, San Francisco, California.
Nisha R AcharyaFrancis I. Proctor Foundation for Research in Ophthalmology, University of California, San Francisco, San Francisco, California.
Brian MadowIra G. Ross Eye Institute, Department of Ophthalmology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo/State University of New York (SUNY), Buffalo, New York.
Jeffrey N ChiangDepartment of Computational Medicine, University of California, Los Angeles, Los Angeles, California.
Edmund TsuiJules Stein Eye Institute, University of California, Los Angeles, Los Angeles, California.

Funding

Objective Measures of Intraocular Inflammation in Pediatric Anterior UveitisK23EY032990 · NEI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI TSUI, EDMUND · 2021 to 2025
$1.2M
NEI NIH HHS K23 EY032990
6 · The paper itself

Abstract

Objective: To assess whether artificial intelligence (AI)-derived fluid volume provides prognostic value for visual outcomes in uveitic macular edema (UME) and to compare model performance to central macular thickness (CMT) measurements alone. Design: Secondary subanalysis of patients with UME in the First-line Antimetabolites as Steroid-sparing Treatment (FAST) clinical trial using a deep learning segmentation model trained on patients with age-related macular degeneration (AMD) and patients with retinal vein occlusion. Participants: Patients with UME secondary to noninfectious uveitis from the FAST Uveitis Trial. Methods: A 2-dimensional U-Net model, trained on patients with AMD and retinal vein occlusion using the RETOUCH data set, was applied to segment intraretinal fluid (IRF) and subretinal fluid (SRF) in Heidelberg Spectralis OCT scans from the FAST Uveitis Trial. Model performance was validated against binary fluid gradings. Linear mixed-effects models evaluated fluid resolution during treatment. Likelihood ratio tests and leave-one-subject-out cross-validation assessed whether baseline IRF and SRF volumes provided improved model fit for visual acuity (VA) change over 6 months of treatment compared to CMT alone. Main Outcome Measures: Segmentation accuracy, correlation of fluid volume with VA, and association of VA change with baseline fluid volumes and CMT versus CMT only. Results: The model achieved Dice scores of 0.61 for IRF and 0.74 for SRF on the RETOUCH data set. In the FAST data set, fluid segmentation correlated with binary gradings (biserial correlation: IRF 0.39, SRF 0.63; Mann-Whitney Conclusions: Artificial intelligence-based segmentation of IRF and SRF enabled quantitative fluid measurements in UME and may provide additional prognostic signal for VA change in UME compared to CMT alone, although larger cohorts will be needed to determine the magnitude of generalizable performance gains, if any. These findings support integrating AI-driven fluid analysis in clinical workflows and suggest that future clinical trials should consider stratifying by baseline fluid characteristics using AI fluid analysis. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Indexed as

Deep learningFluid segmentationMacular edemaRetinal OCTUveitis

Identifiers

PMID42437118
PMCPMC13355740

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.