Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: the CADDementia challenge
Open accessAbstract
Algorithms for computer-aided diagnosis of dementia based on structural MRI have demonstrated high performance in the literature, but are difficult to compare as different data sets and methodology were used for evaluation. In addition, it is unclear how the algorithms would perform on previously unseen data, and thus, how they would perform in clinical practice when there is no real opportunity to adapt the algorithm to the data at hand. To address these comparability, generalizability and clinical applicability issues, we organized a grand challenge that aimed to objectively compare algorithms based on a clinically representative multi-center data set. Using clinical practice as the starting point, the goal was to reproduce the clinical diagnosis. Therefore, we evaluated algorithms for multi-class classification of three diagnostic groups: patients with probable Alzheimer's disease, patients with mild cognitive impairment and healthy controls. The diagnosis based on clinical criteria was used as reference standard, as it was the best available reference despite its known limitations. For evaluation, a previously unseen test set was used consisting of 354 T1-weighted MRI scans with the diagnoses blinded. Fifteen research teams participated with a total of 29 algorithms. The algorithms were trained on a small training set (n=30) and optionally on data from other sources (e.g., the Alzheimer's Disease Neuroimaging Initiative, the Australian Imaging Biomarkers and Lifestyle flagship study of aging). The best performing algorithm yielded an accuracy of 63.0% and an area under the receiver-operating-characteristic curve (AUC) of 78.8%. In general, the best performances were achieved using feature extraction based on voxel-based morphometry or a combination of features that included volume, cortical thickness, shape and intensity. The challenge is open for new submissions via the web-based framework: http://caddementia.grand-challenge.org.
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Traditional machine learning methods are considerably useful for analyzing unified data modalities such as biomarkers, genetic information, neuroimaging, and connected speech.
“traditional ML paradigms have considerable utility in analyzing unified data modalities, including biomarkers,16 genetic information (including emerging polygenic risk scores),17 neuroimaging,18 and connected speech.19”
From Review of Artificial Intelligence for Clinical Use in Alzheimer's Disease and Related Dementias (2026) · cites this paper as [16]
Many AI techniques need sophisticated computational infrastructure that may not be available in typical clinical settings.
“Many AI techniques require sophisticated computational infrastructure that may not be readily available in typical clinical settings.16”
From Review of Artificial Intelligence for Clinical Use in Alzheimer's Disease and Related Dementias (2026) · cites this paper as [16]
Section 3.2 reports computation times for individual dementia-diagnosis algorithms, but does not say they require sophisticated infrastructure or that such infrastructure is unavailable in typical clinics. The Introduction and section 5.1 discuss barriers to clinical implementation involving generalizability, training data, and algorithm availability instead. Searches for hardware, computing resources, and infrastructure found no passage addressing the attributed claim; page numbers were not available in our copy. (LLM, conf=0.72)
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- Authors
- Bron, E. E., Smits, M., van der Flier, W. M., Vrenken, H., Barkhof, F., Scheltens, P., Papma, J. M., Steketee, R. M. E., Méndez Orellana, C., Meijboom, R., Pinto, M., Meireles, J. R., Garrett, C., Bastos-Leite, A. J., Abdulkadir, A., Ronneberger, O., Amoroso, N., Bellotti, R., Cárdenas-Peña, D., Álvarez-Meza, A. M., Dolph, C. V., Iftekharuddin, K. M., Eskildsen, S. F., Coupé, P., Fonov, V. S., Franke, K., Gaser, C., Ledig, C., Guerrero, R., Tong, T., Gray, K. R., Moradi, E., Tohka, J., Routier, A., Durrleman, S., Sarica, A., Di Fatta, G., Sensi, F., Chincarini, A., Smith, G. M., Stoyanov, Z. V., Sørensen, L., Nielsen, M., Tangaro, S., Inglese, P., Wachinger, C., Reuter, M., van Swieten, J. C., Niessen, W. J., & Klein, S.
- Affiliations
- Erasmus MC (NL)
- Amsterdam Neuroscience (NL)
- Hospital de São João (PT)
- Universidade do Porto (PT)
- University of Freiburg (DE)
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari (IT)
- Universidad Nacional de Colombia (CO)
- Old Dominion University (US)
- Aarhus University (DK)
- Montreal Neurological Institute and Hospital (CA)
- Jena University Hospital (DE)
- Imperial College London (GB)
- Tampere University of Technology (FI)
- Centre National de la Recherche Scientifique (FR)
- Magna Graecia University (IT)
- Istituto Nazionale di Fisica Nucleare (IT)
- University of Reading (GB)
- University of Copenhagen (DK)
- Harvard University (US)
- Corresponding author
- Bron, Esther E.
- ORCID
- Bron, Esther E.
- Smits, Marion
- Vrenken, Hugo
- Barkhof, Frederik
- Scheltens, Philip
- Papma, Janne M.
- Steketee, Rebecca M.E.
- Meijboom, Rozanna
- Pinto, Madalena
- Bastos-Leite, António J.
- Abdulkadir, Ahmed
- Ronneberger, Olaf
- Amoroso, Nicola
- Bellotti, Roberto
- Álvarez-Meza, Andrés M.
- Iftekharuddin, Khan M.
- Eskildsen, Simon F.
- Fonov, Vladimir S.
- Franke, Katja
- Ledig, Christian
- Tong, Tong
- Moradi, Elaheh
- Tohka, Jussi
- Routier, Alexandre
- Durrleman, Stanley
- Sarica, Alessia
- Sensi, Francesco
- Chincarini, Andrea
- Sørensen, Lauge
- Nielsen, Mads
- Tangaro, Sabina
- Inglese, Paolo
- Wachinger, Christian
- Reuter, Martin
- Niessen, Wiro J.
- Klein, Stefan
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2015-05
- Volume
- 111
- Pages
- 562-579
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC4943029
- OpenAlex
- W2135011268
- Cited by
- 352
- References
- 68
- Citations by year
- 2026: 21 · 2025: 30 · 2024: 22 · 2023: 29 · 2022: 25 · 2021: 32 · 2020: 28 · 2019: 46 · 2018: 31 · 2017: 39 · 2016: 39 · 2015: 10
- FWCI
- 32.16
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Amsterdam UMC (VU Amsterdam) - Institutional Repository (publishedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (publishedVersion)
- PubMed Central (acceptedVersion)
- CentAUR (University of Reading) (submittedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (submittedVersion)
- RePub (Erasmus University Rotterdam) (submittedVersion, other-oa)
- HAL (submittedVersion, other-oa)
- UCL Discovery (University College London) (submittedVersion)
- Pure Amsterdam UMC (submittedVersion)
- Research at the University of Copenhagen (University of Copenhagen) (submittedVersion)
- CINECA IRIS Institutional Research Information System (University of Bari Aldo Moro) (submittedVersion, cc-by-nc-nd)
- Spiral (Imperial College London) (submittedVersion, cc-by-nc-nd)
- Open Research (University of Surrey) (submittedVersion)
- INFM-OAR (INFN Catania) (submittedVersion, other-oa)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Istituto Nazionale di Fisica Nucleare. Centro Nazionale Analisi Fotogrammi (cc-by)
- Funders
- Alzheimer's Disease Neuroimaging Initiative
- Commonwealth Scientific and Industrial Research Organisation
- National Institutes of Health (U01 AG024904)
- U.S. Department of Defense (W81XWH-12-2-0012)
- 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 › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- computer-aided diagnosis, dementia, Alzheimer's disease, mild cognitive impairment, structural MRI, societal challenges, algorithm comparison, generalizability, multi-center data, voxel-based morphometry, cortical thickness, T1-weighted imaging
- MeSH terms
- Aged, Aged, 80 and over, Algorithms, Alzheimer Disease, Diagnosis, Computer-Assisted, Female, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Male, Middle Aged, Sensitivity and Specificity, Cognitive Dysfunction
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