Deep grading for MRI-based differential diagnosis of Alzheimer’s disease and frontotemporal dementia
Open accessAbstract
Alzheimer's disease and Frontotemporal dementia are common forms of neurodegenerative dementia. Behavioral alterations and cognitive impairments are found in the clinical courses of both diseases, and their differential diagnosis can sometimes pose challenges for physicians. Therefore, an accurate tool dedicated to this diagnostic challenge can be valuable in clinical practice. However, current structural imaging methods mainly focus on the detection of each disease but rarely on their differential diagnosis. In this paper, we propose a deep learning-based approach for both disease detection and differential diagnosis. We suggest utilizing two types of biomarkers for this application: structure grading and structure atrophy. First, we propose to train a large ensemble of 3D U-Nets to locally determine the anatomical patterns of healthy people, patients with Alzheimer's disease and patients with Frontotemporal dementia using structural MRI as input. The output of the ensemble is a 2-channel disease's coordinate map, which can be transformed into a 3D grading map that is easily interpretable for clinicians. This 2-channel disease's coordinate map is coupled with a multi-layer perceptron classifier for different classification tasks. Second, we propose to combine our deep learning framework with a traditional machine learning strategy based on volume to improve the model discriminative capacity and robustness. After both cross-validation and external validation, our experiments, based on 3319 MRIs, demonstrated that our method produces competitive results compared to state-of-the-art methods for both disease detection and differential diagnosis.
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CNNs on MRI and FDG-PET data reported 86.0-94.6% classification accuracy for differentiating FTD, AD, and healthy controls.
“Studies utilizing CNNs on MRI and FDG-PET data have reported high classification accuracies (86.0–94.6%) for differentiating FTD, AD, and healthy controls”
From Multimodal neuroimaging and AI integration in cognitive disorders: advances, challenges, and future directions for precision medicine (2026) · cites this paper as Nguyen et al., 2023
The Method overview and Experimental results sections (page numbers unavailable) describe MRI-based deep learning for classifying AD, FTD, and cognitively normal controls. They do not report the claimed 86.0–94.6% accuracy range or results using FDG-PET; the study says it relies only on structural MRI. (LLM, conf=0.83)
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- Authors
- Nguyen, H.-D., Clément, M., Planche, V., Mansencal, B., & Coupé, P.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- Corresponding author
- Nguyen, Huy-Dung
- Venue
- Artificial Intelligence in Medicine
- Publisher
- Elsevier BV
- Published
- 2023-10
- Volume
- 144
- Pages
- 102636
- ISSN
- 0933-3657 (ISSN-L)
- PMC
- PMC10904714
- OpenAlex
- W4385989015
- Cited by
- 35
- References
- 48
- Citations by year
- 2026: 10 · 2025: 12 · 2024: 13
- FWCI
- 7.13
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (am, 340 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Artificial Intelligence in Medicine (acceptedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion, other-oa)
- HAL (submittedVersion, cc-by-nc-nd)
- Funders
- U.S. Department of Defense (U01 AG024904, W81XWH, W81XWH-12-2-, W81XWH1220012, AG024904)
- Alzheimer's Association (U01 AG024904, P30 AG062421, P30 AG066468, W81XWH-12-2-0012)
- Bristol-Myers Squibb (U01 AG024904, AG024904, W81XWH-12-2-0012)
- Eli Lilly and Company (U01 AG024904, AG024904, W81XWH-12-2-0012)
- Pfizer (AG024904, W81XWH-12-2-0012, U01 AG024904)
- Biogen (W81XWH-12-2-0012, U01 AG024904, AG024904)
- University of Southern California (W81XWH-12-2-0012, U01 AG024904)
- Meso Scale Diagnostics (U01 AG024904, AG024904, W81XWH-12-2-0012)
- Alzheimer's Disease Neuroimaging Initiative (W81XWH-12-2–0012, U01AG024904, AG024904, W81XWH-12-2-)
- BioClinica (U01 AG024904, W81XWH-12-2-0012, AG024904)
- Novartis Pharmaceuticals Corporation (W81XWH-12-2-0012, U01 AG024904, AG024904)
- Northern California Institute for Research and Education (U01 AG024904, W81XWH-12-2-0012)
- Agence Nationale de la Recherche (ANR- 10-IDEX-03-02, 10‐IDEX‐03‐02, 10-LABX-57, ANR18, ANR-10-IDEX, ANR-10LABX-57, ANR-10-IDEX-0003, ANR-18-CE45-0013, ANR-10)
- Eisai (W81XWH-12-2-0012, U01 AG024904, AG024904)
- Centre National de la Recherche Scientifique (ANR-10-IDEX-03-02)
- Servier (AG024904, W81XWH-12-2-0012, U01 AG024904)
- H. Lundbeck A/S (U01 AG024904, AG024904, W81XWH-12-2-0012)
- IXICO (AG024904, W81XWH-12-2-0012, U01 AG024904)
- National Institutes of Health (3u01ag024904-10s2, W81XWH, W81XWH-12-2-0012, AG062421, U01-AG024904, AG024904, AG032306, AG066468, AG062429)
- Genentech (W81XWH-12-2-0012, AG024904, U01 AG024904)
- University of California, San Francisco
- Canadian Institutes of Health Research (U01 AG024904, W81XWH-12-2-0012)
- National Institute on Aging (U01-AG-024904, AG032306, AG066468, AG062429, P30AG010133, P30AG066512, AG062421, W81XWH-12-2–0012, AG 024904, P30AG066506, P20AG068053, P20AG068024, P20AG068077, R01AG032306, P20AG068082, P30AG072978, P30AG066508, P30AG062429, R01AG079280, P30AG072931, P30AG072973, P30AG066468, P30AG066462, P30AG066518, P30AG066444, P30AG072972, P30AG066507, P30AG072976, P30AG072959, P30AG062422, P30AG066515, P30AG066514, P30AG062677, P30AG066509, P30AG072958, P30AG062715, P30AG066530, U24AG072122, P30AG072946, P30AG072977, P30AG072979, P30AG072947, P30AG062421, P30AG066546, P30AG066511, P30AG066519, P30AG072975)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
- Keywords
- Alzheimer's disease, frontotemporal dementia, differential diagnosis, structural MRI, brain atrophy, neurodegenerative dementia, disease detection, multilayer perceptron
- MeSH terms
- Machine Learning, Alzheimer Disease, Diagnosis, Differential, Humans, Magnetic Resonance Imaging, Frontotemporal Dementia
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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