Development and validation of an interpretable deep learning framework for Alzheimer’s disease classification
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Abstract
Alzheimer's disease is the primary cause of dementia worldwide, with an increasing morbidity burden that may outstrip diagnosis and management capacity as the population ages. Current methods integrate patient history, neuropsychological testing and MRI to identify likely cases, yet effective practices remain variably applied and lacking in sensitivity and specificity. Here we report an interpretable deep learning strategy that delineates unique Alzheimer's disease signatures from multimodal inputs of MRI, age, gender, and Mini-Mental State Examination score. Our framework linked a fully convolutional network, which constructs high resolution maps of disease probability from local brain structure to a multilayer perceptron and generates precise, intuitive visualization of individual Alzheimer's disease risk en route to accurate diagnosis. The model was trained using clinically diagnosed Alzheimer's disease and cognitively normal subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset (n = 417) and validated on three independent cohorts: the Australian Imaging, Biomarker and Lifestyle Flagship Study of Ageing (AIBL) (n = 382), the Framingham Heart Study (n = 102), and the National Alzheimer's Coordinating Center (NACC) (n = 582). Performance of the model that used the multimodal inputs was consistent across datasets, with mean area under curve values of 0.996, 0.974, 0.876 and 0.954 for the ADNI study, AIBL, Framingham Heart Study and NACC datasets, respectively. Moreover, our approach exceeded the diagnostic performance of a multi-institutional team of practicing neurologists (n = 11), and high-risk cerebral regions predicted by the model closely tracked post-mortem histopathological findings. This framework provides a clinically adaptable strategy for using routinely available imaging techniques such as MRI to generate nuanced neuroimaging signatures for Alzheimer's disease diagnosis, as well as a generalizable approach for linking deep learning to pathophysiological processes in human disease.
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The authors previously reported interpretable deep learning approaches capable of distinguishing participants with normal cognition from those with AD using MRI scans, age, sex, and MMSE.
“we have previously reported interpretable deep learning approaches capable of distinguishing participants with age-appropriate normal cognition (NC) from those with AD using magnetic resonance imaging (MRI) scans, age, sex, and mini-mental state examination (MMSE)17.”
From Multimodal deep learning for Alzheimer's disease dementia assessment (2022) · cites this paper as [17]
The authors previously developed and externally validated an interpretable deep learning approach to classify AD using multimodal inputs of MRI and clinical variables.
“Previously, we developed and externally validated an interpretable deep learning approach to classify AD using multimodal inputs of MRI and clinical variables17.”
From Multimodal deep learning for Alzheimer's disease dementia assessment (2022) · cites this paper as [17]
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- Authors
- Qiu, S., Joshi, P. S., Miller, M. I., Xue, C., Zhou, X., Karjadi, C., Chang, G. H., Joshi, A. S., Dwyer, B., Zhu, S., Kaku, M., Zhou, Y., Alderazi, Y. J., Swaminathan, A., Kedar, S., Saint-Hilaire, M.-H., Auerbach, S. H., Yuan, J., Sartor, E. A., Au, R., & Kolachalama, V. B.
- Affiliations
- Boston University (US)
- Georgia Institute of Technology (US)
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- The University of Texas Health Science Center at Houston (US)
- University of Nebraska Medical Center (US)
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
- Corresponding author
- Kolachalama, Vijaya B
- ORCID
- Venue
- Brain
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-05-01
- Volume
- 143
- Issue
- 6
- Pages
- 1920-1933
- ISSN
- 0006-8950 (ISSN-L)
- 1460-2156
- PMC
- PMC7296847
- OpenAlex
- W3023079706
- Cited by
- 472
- References
- 35
- Citations by year
- 2026: 58 · 2025: 98 · 2024: 92 · 2023: 76 · 2022: 84 · 2021: 55 · 2020: 8
- FWCI
- 40.68
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Brain (publishedVersion, cc-by-nc)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Funders
- National Center for Advancing Translational Sciences
- National Institutes of Health (1UL1TR001430)
- Scientist Development (17SDG33670323)
- American Heart Association
- Hariri Research Award
- Hariri Institute for Computing and Computational Science & Engineering at Boston University
- Framingham Heart Study’s National Heart, Lung and Blood Institute (N01-HC-25195, HHSN268201500001I)
- NIH (R56-AG062109, AG008122, R01-AG016495, R01-AG033040)
- Boston University’s Affinity Research Collaboratives
- Boston University Alzheimer’s Disease Center (P30-AG013846)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- Alzheimer's disease, Alzheimer's disease diagnosis, explainable deep learning, multimodal neuroimaging, magnetic resonance imaging, fully convolutional network, Mini-Mental State Examination, dementia, postmortem histopathology
- MeSH terms
- Deep Learning, Aged, Aged, 80 and over, Algorithms, Alzheimer Disease, Australia, Brain, Female, Humans, Magnetic Resonance Imaging, Male, Neuropsychological Tests, Models, Statistical, Biomarkers, Disease Progression, Neuroimaging, Cognitive Dysfunction
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