Neuroimaging and Machine Learning for Dementia Diagnosis: Recent Advancements and Future Prospects
Abstract
Dementia, a chronic and progressive cognitive declination of brain function caused by disease or impairment, is becoming more prevalent due to the aging population. A major challenge in dementia is achieving accurate and timely diagnosis. In recent years, neuroimaging with computer-aided algorithms have made remarkable advances in addressing this challenge. The success of these approaches is mostly attributed to the application of machine learning techniques for neuroimaging. In this review paper, we present a comprehensive survey of automated diagnostic approaches for dementia using medical image analysis and machine learning algorithms published in the recent years. Based on the rigorous review of the existing works, we have found that, while most of the studies focused on Alzheimer's disease, recent research has demonstrated reasonable performance in the identification of other types of dementia remains a major challenge. Multimodal imaging analysis deep learning approaches have shown promising results in the diagnosis of these other types of dementia. The main contributions of this review paper are as follows. 1) Based on the detailed analysis of the existing literature, this paper discusses neuroimaging procedures for dementia diagnosis. 2) It systematically explains the most recent machine learning techniques and, in particular, deep learning approaches for early detection of dementia.
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Previous ML methods have largely focused on leveraging neuroimaging data to distinguish individuals with normal cognition from those with mild cognitive impairment and dementia, with AD being the main etiology.
“Previous ML methods have largely focused on leveraging neuroimaging data to distinguish individuals with normal cognition (NC) from those with mild cognitive impairment (MCI) and dementia, with AD being the main etiology given its ubiquity in dementia diagnosis29,30.”
From AI-based differential diagnosis of dementia etiologies on multimodal data (2024) · cites this paper as [29]
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- Authors
- Ahmed, M. R., Zhang, Y., Feng, Z., Lo, B., Inan, O. T., & Liao, H.
- Affiliations
- University of Jinan (CN)
- Imperial College London (GB)
- Georgia Institute of Technology (US)
- Tsinghua University (CN)
- Venue
- IEEE Reviews in Biomedical Engineering
- Published
- 2019
- Volume
- 12
- Pages
- 19-33
- ISSN
- 1937-3333 (ISSN-L)
- 1941-1189
- OpenAlex
- W2905065426
- Cited by
- 142
- References
- 6
- Citations by year
- 2026: 8 · 2025: 23 · 2024: 19 · 2023: 23 · 2022: 27 · 2021: 19 · 2020: 18 · 2019: 5
- FWCI
- 1.62
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Reviews in Biomedical Engineering (publishedVersion)
- PubMed (publishedVersion)
- Spiral (Imperial College London) (submittedVersion)
- Funders
- National Natural Science Foundation of China (61572231, 81427803, 81771940, 61472163)
- National Key Research and Development Program of China (2017YFC0108000)
- Beijing National Science Foundation (7172122, L172003)
- Shandong Provincial Key Research & Development (2017GGX10141)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Keywords
- dementia diagnosis, Alzheimer's disease, neuroimaging, computer-aided diagnosis, deep learning, multimodal imaging, early detection of dementia, medical image analysis, machine learning
- MeSH terms
- Machine Learning, Algorithms, Alzheimer Disease, Dementia, Humans, Image Interpretation, Computer-Assisted, Pattern Recognition, Automated, Early Diagnosis, Neuroimaging, Cognitive Dysfunction, Multimodal Imaging
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