Evidence map›Paper›PMID 38565579›Full record

ArticleScientific reports2024

Neuron-level explainable AI for Alzheimer's Disease assessment from fundus images.

Nooshin Yousefzadeh, Charlie Tran, Adolfo Ramirez-Zamora, Jinghua Chen, Ruogu Fang, My T Thai

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 2 pooled it
9.3field-weighted citation impact, top 2% 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

14 citing papers in PubMed, 2 syntheses or guidelines pooled it, 23 citations in OpenAlex.

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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

6 authors at 1 institution in 1 country.

Nooshin YousefzadehDepartment of Computer and Information Science and Engineering, University of Florida, Gainesville, Florida, USA.
Charlie TranDepartment of Electrical and Computer Engineering, University of Florida, Gainesville, FL, USA.
Adolfo Ramirez-ZamoraDepartment of Neurology, University of Florida, Gainesville, FL, USA.
Jinghua ChenDepartment of Ophthalmology, University of Florida, Gainesville, FL, USA.
Ruogu FangDepartment of Electrical and Computer Engineering, University of Florida, Gainesville, FL, USA. ruogu.fang@bme.ufl.edu.
My T ThaiDepartment of Computer and Information Science and Engineering, University of Florida, Gainesville, Florida, USA. mythai@cise.ufl.edu.
University of Florida · US

Funding

National Science Foundation 2123809
6 · The paper itself

Abstract

Alzheimer's Disease (AD) is a progressive neurodegenerative disease and the leading cause of dementia. Early diagnosis is critical for patients to benefit from potential intervention and treatment. The retina has emerged as a plausible diagnostic site for AD detection owing to its anatomical connection with the brain. However, existing AI models for this purpose have yet to provide a rational explanation behind their decisions and have not been able to infer the stage of the disease's progression. Along this direction, we propose a novel model-agnostic explainable-AI framework, called Granu

Indexed as

Alzheimer DiseaseNeurodegenerative DiseasesFundus OculiHumansMagnetic Resonance ImagingNeuronsRetinaAlzheimer’s DiseaseContinuumDeep learningExplainable-AIUK Biobank

Identifiers

PMID38565579
PMCPMC10987553
OpenAlexW4393442612

What OpenQuestion holds

Textmetadata
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.