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AT(N)-Anchored Multimodal Deep Learning for Alzheimer’s Staging: A Taxonomy and Continuous Burden Index for Low-Resource Settings
Khliq M, Barat C.. Preprints.org, 2026-09-25
Topic: Plasma and spinal-fluid biomarkers
The global burden of dementia is heavily concentrated in low- and middle-income countries (LMICs), yet biological diagnosis of Alzheimer’s Disease (AD) relies on positron emission tomography (PET) and cerebrospinal fluid (CSF) analysis that are operationally inaccessible in these regions. The 2024 Alzheimer’s Association revised criteria recognize sufficiently accurate plasma assays,particularly p-tau217,as Core 1 biomarkers that may support biological diagnosis, offering a scientifically credible pathway to infrastructure-light biological detection and staging. However, the machine learning literature has expanded without principled mapping to the AT(N) biological framework or rigorous consideration of deployability constraints in resource limited settings. We present a systematic scoping review synthesizing 146 included full-text publications (2018–2026) across four multidisciplinary evidence streams, establishing a novel architectural taxonomy that maps deep learning architectural families directly to AT(N) biological targets and connects model inductive biases to specific biomarker categories. We audit major AD datasets, identify critical biases in demographic and biomarker representation, and map pervasive data pathologies—label scarcity, missing modalities, scanner heterogeneity, and population bias—to corresponding ML/DL remediation strategies. Finally, we argue for a shift from binary classification toward continuous, biologically anchored disease-burden scoring, proposing the Alzheimer’s Disease Burden Index (ADBI). The ADBI equations presented herein represent a theoretical and architectural framework designed to guide future prospective validation on multi-ethnic cohorts. This work provides a methodological roadmap for developing globally deployable, equity aware AD diagnostic models.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Khliq, M., & Barat, C. (2026). AT(N)-Anchored Multimodal Deep Learning for Alzheimer’s Staging: A Taxonomy and Continuous Burden Index for Low-Resource Settings. Preprints.org. https://doi.org/10.20944/preprints202609.2240.v1
Vancouver
Khliq M, Barat C. AT(N)-Anchored Multimodal Deep Learning for Alzheimer’s Staging: A Taxonomy and Continuous Burden Index for Low-Resource Settings. Preprints.org. 2026. doi:10.20944/preprints202609.2240.v1.
BibTeX
@article{khliq2026-88ea4149,
author = {Khliq, M. and Barat, C.},
title = {{AT(N)-Anchored Multimodal Deep Learning for Alzheimer’s Staging: A Taxonomy and Continuous Burden Index for Low-Resource Settings}},
journal = {Preprints.org},
year = {2026},
doi = {10.20944/preprints202609.2240.v1},
url = {https://doi.org/10.20944/preprints202609.2240.v1}
}
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.
Key findings
- We present a systematic scoping review synthesizing 146 included full-text publications (2018–2026) across four multidisciplinary evidence streams, establishing a novel architectural taxonomy that maps deep learning architectural families directly to AT(N) biological targets and connects model inductive biases to specific biomarker categories.
- We audit major AD datasets, identify critical biases in demographic and biomarker representation, and map pervasive data pathologies—label scarcity, missing modalities, scanner heterogeneity, and population bias—to corresponding ML/DL remediation strategies.
- Finally, we argue for a shift from binary classification toward continuous, biologically anchored disease-burden scoring, proposing the Alzheimer’s Disease Burden Index (ADBI).
- The ADBI equations presented herein represent a theoretical and architectural framework designed to guide future prospective validation on multi-ethnic cohorts.
- This work provides a methodological roadmap for developing globally deployable, equity aware AD diagnostic models.
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Study details
- Evidence strengthLow
How this study scored 16/100
Preprint — not yet peer-reviewed. It scored 21 on the same rubric, but non-peer-reviewed sources are capped at 16, so it shows as 16. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it. We have no data on 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 and multi-centre collaboration, so those were left out: it earned 13 of the 62 points we could judge it on, which is 21 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 2 / 10 | Tier 4 — not a peer-reviewed journal (Preprints.org) |
| Journal's citation record | 0 / 4 | Not a journal article, so the journal's record does not count |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 4 / 10 | Preprint — not yet peer-reviewed. Hosted on a preprint server. |
| How recent | 5 / 5 | 2026 — within 1 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 | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 13 / 62 | Scored on 12 of 20 parts. We have no data for the rest, so they are left out: 13 of the 62 points we could judge it on is 21 out of 100. |
| Total | 21 / 100 | Capped to 16 because this is not a peer-reviewed article. |
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