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Use of multimodality imaging and artificial intelligence for diagnosis and prognosis of early stages of Alzheimer's disease
Liu X, Chen K, Wu T, Weidman D, Lure F, Li J.. Translational research : the journal of laboratory and clinical medicine, 2018-01-10
Topic: Amyloid, tau and blood-test diagnosis
Alzheimer's disease (AD) is a major neurodegenerative disease and the most common cause of dementia. Currently, no treatment exists to slow down or stop the progression of AD. There is converging belief that disease-modifying treatments should focus on early stages of the disease, that is, the mild cognitive impairment (MCI) and preclinical stages. Making a diagnosis of AD and offering a prognosis (likelihood of converting to AD) at these early stages are challenging tasks but possible with the help of multimodality imaging, such as magnetic resonance imaging (MRI), fluorodeoxyglucose (FDG)-positron emission topography (PET), amyloid-PET, and recently introduced tau-PET, which provides different but complementary information. This article is a focused review of existing research in the recent decade that used statistical machine learning and artificial intelligence methods to perform quantitative analysis of multimodality image data for diagnosis and prognosis of AD at the MCI or preclinical stages. We review the existing work in 3 subareas: diagnosis, prognosis, and methods for handling modality-wise missing data-a commonly encountered problem when using multimodality imaging for prediction or classification. Factors contributing to missing data include lack of imaging equipment, cost, difficulty of obtaining patient consent, and patient drop-off (in longitudinal studies). Finally, we summarize our major findings and provide some recommendations for potential future research directions.
Key findings
- Making a diagnosis of AD and offering a prognosis (likelihood of converting to AD) at these early stages are challenging tasks but possible with the help of multimodality imaging, such as magnetic resonance imaging (MRI), fluorodeoxyglucose (FDG)-positron emission topography (PET), amyloid-PET, and recently introduced tau-PET, which provides different but complementary information.
- This article is a focused review of existing research in the recent decade that used statistical machine learning and artificial intelligence methods to perform quantitative analysis of multimodality image data for diagnosis and prognosis of AD at the MCI or preclinical stages.
- We review the existing work in 3 subareas: diagnosis, prognosis, and methods for handling modality-wise missing data-a commonly encountered problem when using multimodality imaging for prediction or classification.
- Factors contributing to missing data include lack of imaging equipment, cost, difficulty of obtaining patient consent, and patient drop-off (in longitudinal studies).
- Finally, we summarize our major findings and provide some recommendations for potential future research directions.
Why it matters
This review describes how multimodality imaging combined with machine learning and artificial intelligence may help diagnose and predict progression of Alzheimer's disease at mild cognitive impairment or preclinical stages. It concerns diagnostic and prognostic methods and does not test any treatment, cure, or prevention for Alzheimer's disease.
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Study details
- Study designlongitudinal study
- Evidence strengthModerate
How this study scored 51/100
Published in a peer-reviewed journal, and it is a longitudinal study following the same people over time. Scored zero for sample size, effect reported, studied in people and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Translational research : the journal of laboratory and clinical medicine) |
| Journal's citation record | 1 / 4 | Translational research: journal h-index 130 (OpenAlex, 2026-10-29) |
| Study design | 7 / 12 | A longitudinal study following the same people over time |
| Strength of evidence | 3 / 5 | Rated “moderate” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 2 / 5 | 2018 — within 10 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 | 5 / 7 | Cited 142 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 14.2 citations a year over 10 year(s) since 2018 |
| Impact for its field and year | 4 / 6 | Cited 8.5× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 1 / 6 | Cited by 2 other studies in this library |
| Still being cited | 2 / 4 | Cited 46 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Jing Li: h-index 31 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | Arizona State University: institution h-index 987 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 2 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 29352978) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 51 / 100 | — |
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Read the original
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
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
Liu, X., Chen, K., Wu, T., Weidman, D., Lure, F., & Li, J. (2018). Use of multimodality imaging and artificial intelligence for diagnosis and prognosis of early stages of Alzheimer's disease. Translational research : the journal of laboratory and clinical medicine. https://doi.org/10.1016/j.trsl.2018.01.001
Vancouver
Liu X, Chen K, Wu T, Weidman D, Lure F, Li J. Use of multimodality imaging and artificial intelligence for diagnosis and prognosis of early stages of Alzheimer's disease. Translational research : the journal of laboratory and clinical medicine. 2018. doi:10.1016/j.trsl.2018.01.001. PMID: 29352978.
BibTeX
@article{liu2018-29352978,
author = {Liu, X. and Chen, K. and Wu, T. and Weidman, D. and Lure, F. and Li, J.},
title = {{Use of multimodality imaging and artificial intelligence for diagnosis and prognosis of early stages of Alzheimer's disease}},
journal = {Translational research : the journal of laboratory and clinical medicine},
year = {2018},
doi = {10.1016/j.trsl.2018.01.001},
pmid = {29352978},
url = {https://doi.org/10.1016/j.trsl.2018.01.001}
}
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