Evidence map›Paper›PMID 32963300›Full record

ArticleScientific reports2020

Prediction of age-related macular degeneration disease using a sequential deep learning approach on longitudinal SD-OCT imaging biomarkers.

Imon Banerjee, Luis de Sisternes, Joelle A Hallak, Theodore Leng, Aaron Osborne, Philip J Rosenfeld, Giovanni Gregori, Mary Durbin, Daniel Rubin

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
46citing papers in PubMed, 2 pooled it
–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

46 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  7. Radiomics-Based Fundus Photography Analysis in Diabetic Retinopathy.Translational vision science & technology · 2026
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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

9 authors.

Imon Banerjee *Department of Biomedical Informatics, Emory University, Atlanta, GA, 30322, USA. imon.banerjee@emory.edu.ORCID http://orcid.org/0000-0002-3327-8004
Luis de Sisternes *Carl Zeiss Meditec, Inc., Dublin, CA, 94568, USA.
Joelle A HallakDepartment of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, IL, 60612, USA.
Theodore LengByers Eye Institute At Stanford, Stanford University School of Medicine, Palo Alto, CA, 94303, USA.ORCID http://orcid.org/0000-0002-8461-3562
Aaron Osborne, Genentech, CA, 94080, USA.ORCID http://orcid.org/0000-0001-7512-4751
Philip J RosenfeldBascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, FL, 33136, USA.
Giovanni GregoriBascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, FL, 33136, USA.
Mary DurbinCarl Zeiss Meditec, Inc., Dublin, CA, 94568, USA.
Daniel RubinDepartment of Biomedical Data Science, Stanford University, Stanford, CA, 94305, USA.ORCID http://orcid.org/0000-0001-5057-4369

Funding

Stanford Vision Research CoreP30EY026877 · NEI · STANFORD UNIVERSITY · PI Jeffrey L Goldberg · 2017 to 2026
$8.0M
NEI NIH HHS P30 EY026877NIH HHS P30-EY026877
6 · The paper itself

Abstract

We propose a hybrid sequential prediction model called "Deep Sequence", integrating radiomics-engineered imaging features, demographic, and visual factors, with a recursive neural network (RNN) model in the same platform to predict the risk of exudation within a future time-frame in non-exudative AMD eyes. The proposed model provides scores associated with risk of exudation in the short term (within 3 months) and long term (within 21 months), handling challenges related to variability of OCT scan characteristics and the size of the training cohort. We used a retrospective clinical trial dataset that includes 671 AMD fellow eyes with 13,954 observations before any signs of exudation for training and validation in a tenfold cross validation setting. Deep Sequence achieved high performance for the prediction of exudation within 3 months (0.96 ± 0.02 AUCROC) and within 21 months (0.97 ± 0.02 AUCROC) on cross-validation. Training the proposed model on this clinical trial dataset and testing it on an external real-world clinical dataset showed high performance for the prediction within 3-months (0.82 AUCROC) but a clear decrease in performance for the prediction within 21-months (0.68 AUCROC). While performance differences at longer time intervals may be derived from dataset differences, we believe that the high performance and generalizability achieved in short-term predictions may have a high clinical impact allowing for optimal patient follow-up, adding the possibility of more frequent, detailed screening and tailored treatments for those patients with imminent risk of exudation.

Indexed as

AgedBiomarkersClinical Trials as TopicDeep LearningEyeFemaleFluorescein AngiographyHumansMacular DegenerationMaleNeural Networks, ComputerRetrospective StudiesTomography, Optical CoherenceBiomarkers

Identifiers

PMID32963300
PMCPMC7508843

What OpenQuestion holds

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LicenceCC BY
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 OpenQuestion graph.