Evidence map›Paper›PMID 42129262›Full record

ArticleScientific reports2026

UveAI: clinic-ready scoring of retinal inflammation in uveitis on widefield fluorescein angiography using AI.

Victor Amiot, Roberto Pulvirenti, Oscar Jimenez-Del-Toro, Muriel Ott, Teodora-Elena Bogaciu, Shalini Banerjee, Christoph Amstutz, Jean-Marc Odobez, Christophe Chiquet, Yan Guex-Crosier and 5 more

Abstract read
PubMed Full text at PMC Publisher
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

15 authors.

Victor Amiot *Department of Ophthalmology, University of Lausanne, Jules Gonin Eye Hospital, Fondation Asile des Aveugles, Lausanne, Switzerland.
Roberto Pulvirenti *Idiap Research Institute, Martigny, Switzerland.
Oscar Jimenez-Del-ToroIdiap Research Institute, Martigny, Switzerland.
Muriel OttDepartment of Ophthalmology, University of Lausanne, Jules Gonin Eye Hospital, Fondation Asile des Aveugles, Lausanne, Switzerland.
Teodora-Elena BogaciuGrenoble Alpes University, Grenoble, France.
Shalini BanerjeeDepartment of Ophthalmology, Cantonal Hospital Lucerne, Lucerne, Switzerland.
Christoph AmstutzDepartment of Ophthalmology, Cantonal Hospital Lucerne, Lucerne, Switzerland.
Jean-Marc OdobezIdiap Research Institute, Martigny, Switzerland.
Christophe ChiquetGrenoble Alpes University, Grenoble, France.
Yan Guex-CrosierDepartment of Ophthalmology, University of Lausanne, Jules Gonin Eye Hospital, Fondation Asile des Aveugles, Lausanne, Switzerland.
Ciara BerginDepartment of Ophthalmology, University of Lausanne, Jules Gonin Eye Hospital, Fondation Asile des Aveugles, Lausanne, Switzerland.
Ilenia MeloniDepartment of Ophthalmology, University of Lausanne, Jules Gonin Eye Hospital, Fondation Asile des Aveugles, Lausanne, Switzerland.
André AnjosIdiap Research Institute, Martigny, Switzerland.
Florence HoogewoudDepartment of Ophthalmology, University of Lausanne, Jules Gonin Eye Hospital, Fondation Asile des Aveugles, Lausanne, Switzerland.
Mattia TomasoniDepartment of Ophthalmology, University of Lausanne, Jules Gonin Eye Hospital, Fondation Asile des Aveugles, Lausanne, Switzerland. mattia.tomasoni@fa2.ch.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Retinal inflammation is a key determinant of visual prognosis in uveitis, yet its assessment on fluorescein angiography remains subjective, labor-intensive, and insufficiently scalable for clinical trials or large cohort studies. Fluorescein angiography is the gold standard for assessing retinal inflammation. However, its scoring remains challenging, as the process is complex and time-consuming, limiting routine use in clinical trials and patient care. We present UveAI, a modular deep learning framework that grades all major retinal inflammatory signs in fluorescein angiography across posterior pole and periphery to generate an ASUWOG-aligned inflammation score. Trained on 3,220 FA images from 644 eyes (369 patients), UveAI integrates six transformer models detecting macular edema, optic disc hyperfluorescence, and vascular and capillary leakage in the posterior pole and periphery. On an independent test set, UveAI showed high concordance with an expert grader for total score (R = 0.96) and strong performance for individual signs (mean AUC = 0.952). Grad-CAM maps confirmed clinically relevant focus, supporting automated, standardised FA scoring in uveitis.

Indexed as

Fluorescein AngiographyUveitisDeep LearningFemaleHumansInflammationMacular EdemaMaleMiddle AgedCapillaropathyClinical translationDeep learningDisease gradingFluorescein angiographyIntergrader agreementMacular edemaPapillitisUveitisVasculitis

Identifiers

PMID42129262
PMCPMC13369474

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.