Evidence map›Paper›PMID 40582342›Full record

ArticleOphthalmologica. Journal international d'ophtalmologie. International journal of ophthalmology. Zeitschrift fur Augenheilkunde2025

The British-Israeli Project for Algorithm-Based Management of Age-Related Macular Degeneration: Deep Learning Integration for Real-World Data Management and Analysis.

Dinah Zur, David M Wright, Marganit Shahar Gonen, Reut Shor, Qing Wen, Gidi Benyamini, Moshe Havilio, Mor Ben-Nun, Shay Look, Omer Dor and 3 more

Abstract readMulticenter Study
In one paragraph

Article in Ophthalmologica. Journal international d'ophtalmologie. International journal of ophthalmology. Zeitschrift fur Augenheilkunde, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Review
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.

Dinah ZurOphthalmology Division, Tel Aviv Medical Center, Tel Aviv, Israel.
David M WrightCentre for Public Health, Queen's University Belfast, Belfast, UK.
Marganit Shahar GonenOphthalmology Division, Tel Aviv Medical Center, Tel Aviv, Israel.
Reut ShorOphthalmology Division, Tel Aviv Medical Center, Tel Aviv, Israel.
Qing WenCentre for Public Health, Queen's University Belfast, Belfast, UK.
Gidi BenyaminiNotal Vision Ltd, Tel Aviv, Israel.
Moshe HavilioNotal Vision Ltd, Tel Aviv, Israel.
Mor Ben-NunNotal Vision Ltd, Tel Aviv, Israel.
Shay LookOphthalmology Division, Tel Aviv Medical Center, Tel Aviv, Israel.
Omer DorOphthalmology Division, Tel Aviv Medical Center, Tel Aviv, Israel.
Usha ChakravarthyCentre for Public Health, Queen's University Belfast, Belfast, UK.
Anat LoewensteinOphthalmology Division, Tel Aviv Medical Center, Tel Aviv, Israel.
Tunde PetoCentre for Public Health, Queen's University Belfast, Belfast, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionThe aim of this study was to describe the development of an integrative dataset, combining clinical and optical coherence tomography (OCT) imaging data by applying a deep learning (DL) algorithm for automated, objective, and comprehensive quantification of OCT scans in two large real-world datasets of eyes with neovascular age-related macular degeneration (nAMD). We further report baseline characteristics of the study population, focusing on demographics, clinical parameters, and quantitative retinal morphological features.

methodsThis retrospective study analyzed data from 5,207 eyes of 4,265 nAMD patients treated at two centers in the UK and Israel. Longitudinal clinical data and OCT scans were analyzed using a DL algorithm (NOA™, Notal Ltd.) to quantify retinal fluid volumes and morphological features. Baseline characteristics were compared between the cohorts.

resultsThe dataset included 134,340 visual acuity (VA) measurements, 79,457 OCT scans, and 73,218 anti-vascular endothelial growth factor injections. Median follow-up was 4.54 years (UK) and 3.12 years (Israel). Baseline VA differed significantly between cohorts due to varying treatment criteria. Fluid distribution patterns were similar, with most eyes showing combined intraretinal and subretinal fluid. Age-related trends in fluid volumes were observed. Weak correlations were found between baseline OCT measurements and VA.

conclusionThis study demonstrates the feasibility of integrating large-scale clinical and imaging data for automated analysis in nAMD. The comprehensive baseline characterization provides insights into real-world presentations and lays the groundwork for enabling personalized decision-making and optimizing outcomes based on individual patient profiles and fluid distribution patterns.

Indexed as

Deep LearningDisease ManagementFluorescein AngiographyTomography, Optical CoherenceVisual AcuityWet Macular DegenerationAgedAged, 80 and overAngiogenesis InhibitorsFeasibility StudiesFemaleFollow-Up StudiesFundus OculiHumansIntravitreal InjectionsIsraelAngiogenesis InhibitorsVascular Endothelial Growth Factor AAge-related macular degenerationAutomated detectionDeep learningFluid volumesReal-world data

Identifiers

PMID40582342
PMCPMC12453573

What OpenQuestion holds

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LicenceCC BY-NC
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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.