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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. Scientific reports, 2024-04-02
Topic: Plasma and spinal-fluid biomarkers
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 la ̲ r Neuron-le v ̲ el Expl a ̲ iner (LAVA), an interpretation prototype that probes into intermediate layers of the Convolutional Neural Network (CNN) models to directly assess the continuum of AD from the retinal imaging without the need for longitudinal or clinical evaluations. This innovative approach aims to validate retinal vasculature as a biomarker and diagnostic modality for evaluating Alzheimer's Disease. Leveraged UK Biobank cognitive tests and vascular morphological features demonstrate significant promise and effectiveness of LAVA in identifying AD stages across the progression continuum.
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APA 7th
Yousefzadeh, N., Tran, C., Ramirez-Zamora, A., Chen, J., Fang, R., & Thai, M. T. (2024). Neuron-level explainable AI for Alzheimer's Disease assessment from fundus images. Scientific reports. https://doi.org/10.1038/s41598-024-58121-8
Vancouver
Yousefzadeh N, Tran C, Ramirez-Zamora A, Chen J, Fang R, Thai MT. Neuron-level explainable AI for Alzheimer's Disease assessment from fundus images. Scientific reports. 2024. doi:10.1038/s41598-024-58121-8. PMID: 38565579.
BibTeX
@article{yousefzadeh2024-38565579,
author = {Yousefzadeh, Nooshin and Tran, Charlie and Ramirez-Zamora, Adolfo and Chen, Jinghua and Fang, Ruogu and Thai, My T},
title = {{Neuron-level explainable AI for Alzheimer's Disease assessment from fundus images}},
journal = {Scientific reports},
year = {2024},
doi = {10.1038/s41598-024-58121-8},
pmid = {38565579},
url = {https://doi.org/10.1038/s41598-024-58121-8}
}
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Key findings
- Leveraged UK Biobank cognitive tests and vascular morphological features demonstrate significant promise and effectiveness of LAVA in identifying AD stages across the progression continuum.
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Study details
- Study designlongitudinal study
- Evidence strengthModerate
How this study scored 59/100
Published in a peer-reviewed journal, and it is a longitudinal study following the same people over time. Scored zero for sample size, effect reported, studied in people and randomised / blinded because the record does not state it. We have no data on journal's citation record, times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record, multi-centre collaboration and free to read, so those were left out: it earned 33 of the 56 points we could judge it on, which is 59 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Scientific reports) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| Study design | 7 / 12 | A longitudinal study following the same people over time |
| Strength of evidence | 3 / 5 | Rated “moderate” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 4 / 5 | 2024 — within 3 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multi-centre collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 38565579) |
| Free to read | — / 2 | Access not recorded — left out of this score |
| Points | 33 / 56 | Scored on 10 of 20 parts. We have no data for the rest, so they are left out: 33 of the 56 points we could judge it on is 59 out of 100. |
| Total | 59 / 100 | — |
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