Evidence map›Paper›PMID 40922557›Full record

Trial reportClinical & experimental ophthalmology

Deep Learning-Based Detection of Reticular Pseudodrusen in Age-Related Macular Degeneration.

Himeesh Kumar, Yelena Bagdasarova, Scott Song, Doron G Hickey, Amy C Cohn, Mali Okada, Robert P Finger, Jan H Terheyden, Ruth E Hogg, Pierre-Henry Gabrielle and 17 more

Abstract readMulticenter StudyRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in Clinical & experimental ophthalmology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Trial
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

27 authors.

Himeesh KumarCentre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia.
Yelena BagdasarovaDepartment of Ophthalmology, University of Washington, Seattle, Washington, USA.
Scott SongDepartment of Ophthalmology, University of Washington, Seattle, Washington, USA.
Doron G HickeyCentre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia.
Amy C CohnCentre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia.ORCID 0000-0003-3572-6760
Mali OkadaCentre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia.
Robert P FingerDepartment of Ophthalmology, University Hospital Bonn, Bonn, Germany.
Jan H TerheydenDepartment of Ophthalmology, University Hospital Bonn, Bonn, Germany.
Ruth E HoggCentre for Public Health, Queen's University Belfast Faculty of Medicine Health and Life Sciences, Belfast, UK.
Pierre-Henry GabrielleDepartment of Ophthalmology, Dijon University Hospital, Dijon, France.ORCID 0000-0002-9688-4845
Louis ArnouldDepartment of Ophthalmology, Dijon University Hospital, Dijon, France.
Maxime JannaudCentre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia.
Xavier HadouxCentre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia.
Peter van WijngaardenCentre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia.ORCID 0000-0002-8800-7834
Carla J AbbottCentre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia.ORCID 0000-0002-1432-8977
Lauren A B HodgsonCentre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia.
Roy SchwartzMoorfields Eye Hospital NHS Foundation Trust, London, UK.
Adnan TufailMoorfields Eye Hospital NHS Foundation Trust, London, UK.
Emily Y ChewDivision of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, Maryland, USA.ORCID 0000-0003-0999-9802
Cecilia S LeeDepartment of Ophthalmology, University of Washington, Seattle, Washington, USA.
Erica L FletcherDepartment of Anatomy and Physiology, The University of Melbourne, Melbourne, Victoria, Australia.
Melanie BahloPopulation Health and Immunity Division, Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.
Brendan R E AnsellPopulation Health and Immunity Division, Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.
Alice PébayDepartment of Anatomy and Physiology, The University of Melbourne, Melbourne, Victoria, Australia.
Robyn H GuymerCentre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia.ORCID 0000-0002-9441-4356
Aaron Y LeeDepartment of Ophthalmology, University of Washington, Seattle, Washington, USA.
Zhichao WuCentre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia.ORCID 0000-0002-8623-6684

Funding

Aging eyes and aging brains in studying alzheimer's disease: Modern ophthalmic data collection in the adult changes in thought (ACT) studyR01AG060942 · NIA · WASHINGTON UNIVERSITY · PI Cecilia Sungmin Lee · 2019 to 2026
$39.4M
Bridge2AI:Salutogenesis Data Generation ProjectOT2OD032644 · OD · WASHINGTON UNIVERSITY · PI AYYAGARI, RADHA, BAHMANI, AMIR · 2022 to 2025
$32.7M
Macular Disease Foundation AustraliaNational Health and Medical Research Council APP1027624National Health and Medical Research Council GNT1181010National Health and Medical Research Council GNT1194667National Health and Medical Research Council GNT2008382NIA NIH HHS R01 AG060942NIH HHS OT2 OD032644NIH HHS OT2OD32644NIH HHS R01AG060942Research to Prevent Blindness
6 · The paper itself

Abstract

backgroundReticular pseudodrusen (RPD) signify a critical phenotype driving vision loss in age-related macular degeneration (AMD). This study sought to develop and externally test a deep learning (DL) model to detect RPD on optical coherence tomography (OCT) scans with expert-level performance.

methodsRPD were manually segmented in 9800 OCT B-scans from individuals enrolled in a multicentre randomised trial. A DL model for instance segmentation of RPD was developed and evaluated against four retinal specialists in an internal test dataset. The primary outcome was the performance of the DL model for detecting RPD in OCT volumes in five external test datasets compared to two retinal specialists.

resultsIn an internal test dataset consisting of 250 OCT B-scans, the DL model produced RPD segmentations that had higher agreement with four retinal specialists (Dice similarity coefficient [DSC] = 0.76) than the agreement amongst the specialists (DSC = 0.68; p < 0.001). In the five external test datasets consisting of 1017 eyes from 812 individuals, the DL model detected RPD in OCT volumes with a similar level of performance as two retinal specialists (area under the receiver operator characteristic curve [AUC] = 0.94, 0.95 and 0.96 respectively; p ≥ 0.32).

conclusionsWe present a DL model for automatic detection of RPD with expert-level performance, which could be used to support the clinical management of AMD. This model has been made publicly available to facilitate future research to understand this critical, yet enigmatic, AMD phenotype.

Indexed as

Deep LearningMacular DegenerationRetinal DrusenTomography, Optical CoherenceAgedAged, 80 and overFemaleHumansMaleMiddle AgedROC Curveage‐related macular degenerationdeep learningoptical coherence tomographyreticular pseudodrusenretinal drusen

Identifiers

PMID40922557
PMCPMC12886592

What OpenQuestion holds

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LicenceCC BY
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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.