Evidence map›Paper›PMID 40001659›Full record

ArticleBioengineering (Basel, Switzerland)2025

Deep Learning Approach Predicts Longitudinal Retinal Nerve Fiber Layer Thickness Changes.

Jalil Jalili, Evan Walker, Christopher Bowd, Akram Belghith, Michael H Goldbaum, Massimo A Fazio, Christopher A Girkin, Carlos Gustavo De Moraes, Jeffrey M Liebmann, Robert N Weinreb and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Bioengineering (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
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

12 authors.

Jalil JaliliHamilton Glaucoma Center and Division of Ophthalmology Informatics and Data Science, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, CA 92037, USA.ORCID 0000-0002-7265-8675
Evan WalkerHamilton Glaucoma Center and Division of Ophthalmology Informatics and Data Science, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, CA 92037, USA.ORCID 0000-0001-8566-0371
Christopher BowdHamilton Glaucoma Center and Division of Ophthalmology Informatics and Data Science, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, CA 92037, USA.ORCID 0000-0001-9330-9987
Akram BelghithHamilton Glaucoma Center and Division of Ophthalmology Informatics and Data Science, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, CA 92037, USA.
Michael H GoldbaumHamilton Glaucoma Center and Division of Ophthalmology Informatics and Data Science, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, CA 92037, USA.
Massimo A FazioDepartment of Ophthalmology and Vision Sciences, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35233, USA.ORCID 0000-0002-7489-089X
Christopher A GirkinHamilton Glaucoma Center and Division of Ophthalmology Informatics and Data Science, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, CA 92037, USA.
Carlos Gustavo De MoraesBernard and Shirlee Brown Glaucoma Research Laboratory, Department of Ophthalmology, Edward S. Harkness Eye Institute, Columbia University Medical Center, New York, NY 10032, USA.
Jeffrey M LiebmannBernard and Shirlee Brown Glaucoma Research Laboratory, Department of Ophthalmology, Edward S. Harkness Eye Institute, Columbia University Medical Center, New York, NY 10032, USA.
Robert N WeinrebHamilton Glaucoma Center and Division of Ophthalmology Informatics and Data Science, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, CA 92037, USA.ORCID 0000-0001-9553-3202
Linda M ZangwillHamilton Glaucoma Center and Division of Ophthalmology Informatics and Data Science, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, CA 92037, USA.
Mark ChristopherHamilton Glaucoma Center and Division of Ophthalmology Informatics and Data Science, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, CA 92037, USA.

Funding

Bridge2AI:Salutogenesis Data Generation ProjectOT2OD032644 · OD · WASHINGTON UNIVERSITY · PI AYYAGARI, RADHA, BAHMANI, AMIR · 2022 to 2025
$32.7M
Vision BiostatisticsP30EY022589 · NEI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Derek Stuart Welsbie · 2012 to 2026
$10.3M
Diagnostic Innovations in Glaucoma:Structural AssessmentR01EY011008 · NEI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI ZANGWILL, LINDA M · 1995 to 2015
$8.7M
Diagnostic Innovations in Glaucoma Study (DIGS): High Myopia and Advanced DiseaseR01EY027510 · NEI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI LINDA M ZANGWILL · 2017 to 2026
$5.3M
Diagnosis and Monitoring of Glaucoma with Optical Coherence Tomography AngiographyR01EY029058 · NEI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI WEINREB, ROBERT N · 2018 to 2024
$4.2M
African Descent and Glaucoma Evaluation (ADAGES) II: Glaucoma ProgressionR01EY019869 · NEI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI ZANGWILL, LINDA M · 2010 to 2014
$2.8M
African Descent and Glaucoma Evaluation (ADAGES) IV: Alterations of the lamina cribrosa in progressionR01EY026574 · NEI · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI FAZIO, MASSIMO ANTONIO, GIRKIN, CHRISTOPHER ANTHONY · 2017 to 2020
$2.6M
Multimodal Artificial Intelligence to Predict Glaucomatous Progression and Surgical InterventionR01EY034146 · NEI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI BAXTER, SALLY LIU, CHRISTOPHER, MARK · 2022 to 2025
$1.8M
Translational Vision Research Training at UCSDT32EY026590 · NEI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI ZANGWILL, LINDA M · 2016 to 2025
$854k
Deep Learning Approaches to Detect Glaucoma and Predict Progression from Spectral Domain Optical Coherence TomographyR00EY030942 · NEI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI CHRISTOPHER, MARK · 2023 to 2025
$747k
AI-Based Identification of Rapid Glaucoma Progression to Guide Clinical Management and Accelerate Clinical TrialsR41EY034424 · NEI · AISIGHT HEALTH, INC. · PI TAFRESHI, ALI, ZANGWILL, LINDA M · 2022 to 2022
$240k
Deep Learning Approaches to Detect Glaucoma and Predict Progression from Spectral Domain Optical Coherence TomographyK99EY030942 · NEI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI CHRISTOPHER, MARK · 2020 to 2021
$235k
NEI NIH HHS K99 EY030942NEI NIH HHS P30 EY022589NEI NIH HHS R00 EY030942NEI NIH HHS R00EY030942, R01EY027510, R01EY034146 R01EY11008, P30EY022589, R01EY026590, EY022039, EY021818, R01EY023704, R01EY029058, EY19869, R21 EY027945, T35 EY033704NEI NIH HHS R01 EY011008NEI NIH HHS R01 EY019869NEI NIH HHS R01 EY026574NEI NIH HHS R01 EY027510NEI NIH HHS R01 EY029058NEI NIH HHS R01 EY034146NEI NIH HHS R41 EY034424NEI NIH HHS T32 EY026590NEI NIH HHS T35 EY033704NIH HHS OT2OD032644The Glaucoma Foundation. Unrestricted grant from Research to Prevent Blindness (New York, NY).
6 · The paper itself

Abstract

This study aims to develop deep learning (DL) models to predict the retinal nerve fiber layer (RNFL) thickness changes in glaucoma, facilitating the early diagnosis and monitoring of disease progression. Using the longitudinal data from two glaucoma studies (Diagnostic Innovations in Glaucoma Study (DIGS) and African Descent and Glaucoma Evaluation Study (ADAGES)), we constructed models using optical coherence tomography (OCT) scans from 251 participants (437 eyes). The models were trained to predict the RNFL thickness at a future visit based on previous scans. We evaluated four models: linear regression (LR), support vector regression (SVR), gradient boosting regression (GBR), and a custom 1D convolutional neural network (CNN). The GBR model achieved the best performance in predicting pointwise RNFL thickness changes (MAE = 5.2 μm, R

Indexed as

deep learningglaucomalongitudinal OCTone-dimensional convolutional neural networkoptical coherence tomographyRNFL thickness prediction

Identifiers

PMID40001659
PMCPMC11851649

What OpenQuestion holds

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Registered trials

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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.