Evidence map›Paper›PMID 36043139›Full record

ArticleBrain communications2022

Deep-learning retinal vessel calibre measurements and risk of cognitive decline and dementia.

Carol Y Cheung, Win Lee Edwin Wong, Saima Hilal, Cheuk Ni Kan, Bibek Gyanwali, Yih Chung Tham, Leopold Schmetterer, Dejiang Xu, Mong Li Lee, Wynne Hsu and 4 more

Open access · goldAbstract read
In one paragraph

Article in Brain communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 1 pooled it
4.9field-weighted citation impact, top 4% of its field
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

26 citing papers in PubMed, 1 synthesis or guideline pooled it, 37 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Article
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  5. Article
  6. Article
  7. Article
  8. A deep-learning retinal aging biomarker for cognitive decline and incident dementia.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025
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  9. Review
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  14. Alzheimer's disease pathophysiology in the Retina.Progress in retinal and eye research · 2024
    Review
  15. Review
  16. Article
  17. Article
  18. Accurate estimation of biological age and its application in disease prediction using a multimodal image Transformer system.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  19. Article
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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

14 authors at 6 institutions in 4 countries.

Carol Y CheungDepartment of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China.ORCID https://orcid.org/0000-0002-9672-1819
Win Lee Edwin WongDepartment of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117600, Singapore.
Saima HilalDepartment of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117600, Singapore.
Cheuk Ni KanDepartment of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117600, Singapore.
Bibek GyanwaliDepartment of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117600, Singapore.
Yih Chung ThamSingapore Eye Research Institute, Singapore National Eye Centre, Singapore 169856, Singapore.
Leopold SchmettererSingapore Eye Research Institute, Singapore National Eye Centre, Singapore 169856, Singapore.
Dejiang XuSchool of Computing, National University of Singapore, Singapore 117417, Singapore.
Mong Li LeeSchool of Computing, National University of Singapore, Singapore 117417, Singapore.ORCID https://orcid.org/0000-0002-9636-388X
Wynne HsuSchool of Computing, National University of Singapore, Singapore 117417, Singapore.ORCID https://orcid.org/0000-0002-4142-8893
Narayanaswamy VenketasubramanianRaffles Neuroscience Centre, Raffles Hospital, Singapore, Singapore 188770, Singapore.
Boon Yeow TanSt. Luke's Hospital, Singapore, Singapore 659674, Singapore.
Tien Yin WongSingapore Eye Research Institute, Singapore National Eye Centre, Singapore 169856, Singapore.
Christopher P L H ChenDepartment of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117600, Singapore.
National University of Singapore · SGSingapore National Eye Center · SGChinese University of Hong Kong · HKNanyang Technological University · SGRaffles Institution · SGSt Luke's Hospital · SG

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Previous studies have explored the associations of retinal vessel calibre, measured from retinal photographs or fundus images using semi-automated computer programs, with cognitive impairment and dementia, supporting the concept that retinal blood vessels reflect microvascular changes in the brain. Recently, artificial intelligence deep-learning algorithms have been developed for the fully automated assessment of retinal vessel calibres. Therefore, we aimed to determine whether deep-learning-based retinal vessel calibre measurements are predictive of risk of cognitive decline and dementia. We conducted a prospective study recruiting participants from memory clinics at the National University Hospital and St. Luke's Hospital in Singapore; all participants had comprehensive clinical and neuropsychological examinations at baseline and annually for up to 5 years. Fully automated measurements of retinal arteriolar and venular calibres from retinal fundus images were estimated using a deep-learning system. Cox regression models were then used to assess the relationship between baseline retinal vessel calibre and the risk of cognitive decline and developing dementia, adjusting for age, gender, ethnicity, education, cerebrovascular disease status, hypertension, hyperlipidemia, diabetes, and smoking. A total of 491 participants were included in this study, of whom 254 developed cognitive decline over 5 years. In multivariable models, narrower retinal arteriolar calibre (hazard ratio per standard deviation decrease = 1.258,

Indexed as

cognitive declinedeep-learning systemdementiaretinal imagingretinal vessel calibre

Identifiers

PMID36043139
PMCPMC9416061
OpenAlexW4292289544

What OpenQuestion holds

Textmetadata
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.