Evidence map›Paper›PMID 41625481›Full record

ArticleCerebral circulation - cognition and behavior2026

Deep learning retinal imaging model identifies poor brain health among older adults without dementia.

Bonnie Yin Ka Lam, An Ran Ran, Yuan Cai, Huijing Zheng, Jize Wei, Yuen Tung Ng, Michael Ying Hong Chan, Ha Ying Chiu, Ho Ko, Carol Y Cheung and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cerebral circulation - cognition and behavior, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

11 authors.

Bonnie Yin Ka LamDivision of Neurology, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR.
An Ran RanDepartment of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR.
Yuan CaiDivision of Neurology, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR.
Huijing ZhengDivision of Neurology, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR.
Jize WeiDivision of Neurology, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR.
Yuen Tung NgDivision of Neurology, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR.
Michael Ying Hong ChanDivision of Neurology, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR.
Ha Ying ChiuCharles K Kao Foundation for Alzheimer's disease.
Ho KoDivision of Neurology, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR.
Carol Y CheungDepartment of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR.
Vincent Chung Tong MokDivision of Neurology, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: The i-Cog Brain Health is a validated deep learning model for differentiating Alzheimer's disease dementia from cognitively unimpaired subjects based on retinal photographs. This study aimed to apply the i-Cog Brain Health in subjects without dementia and assess whether this tool may detect alterations in the retinal vessel network in healthy older adults. Methods: Community subjects were recruited from the BEAT AD ( Results: Among the 185 subjects (mean age: 68.14 ± 5.17 years; males: 32.97%), 29 (15.68%) were classified as positive by i-Cog Brain Health. Those subjects were significantly older ( Conclusions: The i-Cog Brain Health reflected older age and wider venular branching width, which are associated with dementia. The i-Cog Brain Health showed the potential to differentiate retinal features associated with dementia at an early stage and serve as a risk stratification tool.

Indexed as

Alzheimer’s diseaseDeep learning modelRetinal imagingSmall vessel diseaseVascular imaging

Identifiers

PMID41625481
PMCPMC12857183

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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.