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Biomarker fidelity score: A quantitative framework for individual-level validation of explainability methods in 3D Alzheimer’s disease MRI classification
Lepcha DC, Ali A, Martin SA, Syed Abdul S.. bioRxiv, 2026-08-20
Topic: Brain imaging, connectivity and assessment
Explainability methods applied to deep learning models for Alzheimer’s disease neuroimaging produce attribution maps that vary substantially across methods and architectures, yet no validated quantitative framework exists for determining which method most faithfully localises attribution signal within established AD neuroimaging biomarker anatomy at the individual subject level. Existing validation approaches rely on group-level comparisons against meta-analytic activation maps or qualitative visual inspection, leaving individual-level biomarker alignment uncharacterised across the full cognitively normal, mild cognitive impairment, and AD diagnostic spectrum. We introduce the Biomarker Fidelity Score (BFS), a quantitative clinical AI validation tool that measures the spatial overlap between individual-level three-dimensional explainability attention maps and atlas-registered AD-relevant neuroimaging biomarker regions of interest across thirteen anatomically defined structures including the hippocampus, entorhinal cortex, amygdala, and parahippocampal gyrus. Five established explainability methods (GradCAM++, Integrated Gradients, DeepSHAP, Layer-wise Relevance Propagation, and ScoreCAM) were benchmarked across three volumetric architectures (3D ResNet-18, DenseNet-121, and Swin-UNETR) on 327 balanced ADNI-3 subjects. Integrated Gradients achieved the highest BFS across all three architectures while GradCAM++ consistently showed the lowest biomarker alignment (all p<0.001, Friedman test). The complete BFS pipeline, applied without retraining, replicated these method rankings with near-perfect fidelity on an independent cohort of 207 OASIS-3 subjects, with a maximum absolute difference of 0.0005 across all fifteen method-architecture combinations and a Spearman rank correlation of 0.964 between cohort rankings, providing rare direct evidence that XAI method reliability generalises across scanners, acquisition protocols, and populations. By offering an externally validated, individual-level, biomarker-grounded quantitative standard, the BFS framework equips clinicians and AI developers with practical translational guidance for selecting trustworthy explainability methods in AD neuroimaging, directly supporting the responsible clinical deployment of explainable AI.
Key findings
- We introduce the Biomarker Fidelity Score (BFS), a quantitative clinical AI validation tool that measures the spatial overlap between individual-level three-dimensional explainability attention maps and atlas-registered AD-relevant neuroimaging biomarker regions of interest across thirteen anatomically defined structures including the hippocampus, entorhinal cortex, amygdala, and parahippocampal gyrus.
- Five established explainability methods (GradCAM++, Integrated Gradients, DeepSHAP, Layer-wise Relevance Propagation, and ScoreCAM) were benchmarked across three volumetric architectures (3D ResNet-18, DenseNet-121, and Swin-UNETR) on 327 balanced ADNI-3 subjects.
- Integrated Gradients achieved the highest BFS across all three architectures while GradCAM++ consistently showed the lowest biomarker alignment (all p<0.001, Friedman test).
- The complete BFS pipeline, applied without retraining, replicated these method rankings with near-perfect fidelity on an independent cohort of 207 OASIS-3 subjects, with a maximum absolute difference of 0.0005 across all fifteen method-architecture combinations and a Spearman rank correlation of 0.964 between cohort rankings, providing rare direct evidence that XAI method reliability generalises across scanners, acquisition protocols, and populations.
- By offering an externally validated, individual-level, biomarker-grounded quantitative standard, the BFS framework equips clinicians and AI developers with practical translational guidance for selecting trustworthy explainability methods in AD neuroimaging, directly supporting the responsible clinical deployment of explainable AI.
Study details
- Evidence strengthModerate
How this study scored 16/100
Preprint — not yet peer-reviewed. It scored 24 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 15 of the 62 points we could judge it on, which is 24 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 2 / 10 | Tier 4 — not a peer-reviewed journal (bioRxiv) |
| 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 | 3 / 5 | Rated “moderate” 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 | 15 / 62 | Scored on 12 of 20 parts. We have no data for the rest, so they are left out: 15 of the 62 points we could judge it on is 24 out of 100. |
| Total | 24 / 100 | Capped to 16 because this is not a peer-reviewed article. |
What each of these 20 components measures, and what this score is not →
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Cite this study APA, Vancouver, BibTeX · download .bib or .ris
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.
APA 7th
Lepcha, D. C., Ali, A., Martin, S. A., & Syed Abdul, S. (2026). Biomarker fidelity score: A quantitative framework for individual-level validation of explainability methods in 3D Alzheimer’s disease MRI classification. bioRxiv. https://doi.org/10.64898/2026.08.15.744687
Vancouver
Lepcha DC, Ali A, Martin SA, Syed Abdul S. Biomarker fidelity score: A quantitative framework for individual-level validation of explainability methods in 3D Alzheimer’s disease MRI classification. bioRxiv. 2026. doi:10.64898/2026.08.15.744687.
BibTeX
@article{lepcha2026-b6193857,
author = {Lepcha, D. C. and Ali, A. and Martin, S. A. and Syed Abdul, S.},
title = {{Biomarker fidelity score: A quantitative framework for individual-level validation of explainability methods in 3D Alzheimer’s disease MRI classification}},
journal = {bioRxiv},
year = {2026},
doi = {10.64898/2026.08.15.744687},
url = {https://doi.org/10.64898/2026.08.15.744687}
}
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