Evidence map›Paper›PMID 41483865›Full record

ArticleAmerican journal of ophthalmology2026

An Artificial Intelligence-Based Prognostic Model for Prediction of Functional Glaucoma Progression From Clinical and Structural Data.

Vahid Mohammadzadeh, Sean Wu, Sajad Besharati, Mahshad Rafiee, Yasamin Banaei, Arthur Martinyan, Jane Zou, Evelyn Kung, Kiumars Edalati, Esteban Morales and 3 more

Abstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in American journal of ophthalmology, 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

13 authors.

Vahid MohammadzadehFrom the Glaucoma Division (V.M., S.B., M.R., Y.B., A.M., J.Z., E.K., K.E., E.M., J.C., K.N.M.), Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA; Department of Ophthalmology and Vision Sciences (V.M.), University of Louisville, Louisville, Kentucky, USA.
Sean WuDepartment of Computer Science (S.W., F.S.), Pepperdine University, Malibu, California, USA.
Sajad BesharatiFrom the Glaucoma Division (V.M., S.B., M.R., Y.B., A.M., J.Z., E.K., K.E., E.M., J.C., K.N.M.), Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA.
Mahshad RafieeFrom the Glaucoma Division (V.M., S.B., M.R., Y.B., A.M., J.Z., E.K., K.E., E.M., J.C., K.N.M.), Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA.
Yasamin BanaeiFrom the Glaucoma Division (V.M., S.B., M.R., Y.B., A.M., J.Z., E.K., K.E., E.M., J.C., K.N.M.), Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA.
Arthur MartinyanFrom the Glaucoma Division (V.M., S.B., M.R., Y.B., A.M., J.Z., E.K., K.E., E.M., J.C., K.N.M.), Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA.
Jane ZouFrom the Glaucoma Division (V.M., S.B., M.R., Y.B., A.M., J.Z., E.K., K.E., E.M., J.C., K.N.M.), Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA.
Evelyn KungFrom the Glaucoma Division (V.M., S.B., M.R., Y.B., A.M., J.Z., E.K., K.E., E.M., J.C., K.N.M.), Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA.
Kiumars EdalatiFrom the Glaucoma Division (V.M., S.B., M.R., Y.B., A.M., J.Z., E.K., K.E., E.M., J.C., K.N.M.), Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA.
Esteban MoralesFrom the Glaucoma Division (V.M., S.B., M.R., Y.B., A.M., J.Z., E.K., K.E., E.M., J.C., K.N.M.), Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA.
Fabien ScalzoDepartment of Computer Science (S.W., F.S.), Pepperdine University, Malibu, California, USA; Department of Computer Science (F.S.), University of California Los Angeles, Los Angeles, California, USA.
Joseph CaprioliFrom the Glaucoma Division (V.M., S.B., M.R., Y.B., A.M., J.Z., E.K., K.E., E.M., J.C., K.N.M.), Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA.
Kouros Nouri-MahdaviFrom the Glaucoma Division (V.M., S.B., M.R., Y.B., A.M., J.Z., E.K., K.E., E.M., J.C., K.N.M.), Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA. Electronic address: nourimahdavik@gmail.com.

Funding

Detection of Disease Progression in Advanced GlaucomaR01EY029792 · NEI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI NOURI-MAHDAVI, KOUROS · 2020 to 2024
$1.9M
NEI NIH HHS R01 EY029792
6 · The paper itself

Abstract

purposeIntegration of various sources of information for prediction of disease progression is an unmet need in glaucoma diagnostics. We designed a deep learning-based prognostic model incorporating clinical and structural data for forecasting functional glaucoma progression and compared its performance to clinicians.

designRetrospective, comparative cohort study of prognostic accuracy. SUBJECTS: We included 1599 eyes (908 patients) with definite or suspected glaucoma with ≥5 24-2 visual fields (VF) and 3 or more years of follow-up.

methodsVF mean deviation (MD) rates of change were estimated with linear regression. Sequential MD rates of change were estimated with each series spanning only 5 years of follow-up. VF progression was declared when four sequential statistically significant negative MD slopes were observed, and slope for the entire follow-up was significant. A convolutional neural network pretrained on ImageNet was designed to predict VF progression using baseline clinical and demographic data, disc photographs, and optical coherence tomography-derived global and sectoral retinal nerve fiber layer and macular thickness measurements. In addition, average intraocular pressure and treatment information during follow-up were put into the model. The same data for a subset of patients was provided to two clinicians to independently predict future progression. The model was validated on a separate cohort of eyes in which optical coherence tomography imaging was done with a different device (291 eyes).

main outcome measuresModel's area under receiver operating characteristic curves (AUC), accuracy, and area under the precision and recall curves.

resultsAverage (SD) baseline MD and number of VF exams were -3.5 (4.9) dB and 10.1 (4.7). 399 eyes (25%) deteriorated. The best-performing model incorporated baseline disc photographs, and retinal nerve fiber layer and macular thickness: AUC, 0.839 (0.771-0.906), accuracy, 76.0% (62.0%-85.0%), and area under the precision and recall curves, 0.558 (0.385-0.733). Deep learning model significantly outperformed clinical graders (AUC : 0.629 [0.531-0738], P < .001 and 0.680 [0.584-0.776], P = .001, for grader one and two, respectively). Model performance was similar on the validation cohort (AUC: 0.754 [0.671-0.837], and accuracy: 77% [71%-82%], respectively, P = .122). The model performed well when predicting fast-progression, defined as MD rate <-1.0 dB/y (AUC: 0.869 [0.792-0.947]).

conclusionsOur newly designed deep learning model can combine baseline demographic and clinical data with widely available structural measurements and provide clinically relevant information for the prediction of glaucoma progression.

Indexed as

Artificial IntelligenceDeep LearningGlaucomaIntraocular PressureOptic DiskRetinal Ganglion CellsVisual FieldsAgedDisease ProgressionFemaleFollow-Up StudiesHumansMaleMiddle AgedNerve FibersPrognosis

Identifiers

PMID41483865
PMCPMC13379235

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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.