The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
Machine learning of neuroimaging for assisted diagnosis of cognitive impairment and dementia: A systematic review
Open accessAbstract
Introduction: Advanced machine learning methods might help to identify dementia risk from neuroimaging, but their accuracy to date is unclear. Methods: We systematically reviewed the literature, 2006 to late 2016, for machine learning studies differentiating healthy aging from dementia of various types, assessing study quality, and comparing accuracy at different disease boundaries. Results: Of 111 relevant studies, most assessed Alzheimer's disease versus healthy controls, using AD Neuroimaging Initiative data, support vector machines, and only T1‐weighted sequences. Accuracy was highest for differentiating Alzheimer's disease from healthy controls and poor for differentiating healthy controls versus mild cognitive impairment versus Alzheimer's disease or mild cognitive impairment converters versus nonconverters. Accuracy increased using combined data types, but not by data source, sample size, or machine learning method. Discussion: Machine learning does not differentiate clinically relevant disease categories yet. More diverse data sets, combinations of different types of data, and close clinical integration of machine learning would help to advance the field.
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Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: a systematic review
Borchert, R. J., Azevedo, T., Badhwar, A., Bernal, J., Betts, M., Bruffaerts, R., Burkhart, M. C., Dewachter, I., Gellersen, H. M., Low, A., et al. (2023) · Alzheimer's & Dementia · doi:10.1002/alz.13412 · Cited by 125
Metadata
- Authors
- Pellegrini, E., Ballerini, L., Hernandez, M. D. C. V., Chappell, F. M., González‐Castro, V., Anblagan, D., Danso, S., Muñoz‐Maniega, S., Job, D., Pernet, C., Mair, G., MacGillivray, T. J., Trucco, E., & Wardlaw, J. M.
- Venue
- Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring
- Publisher
- Wiley
- Published
- 2018-01
- Volume
- 10
- Issue
- 1
- Pages
- 519-535
- ISSN
- 2352-8729 (ISSN-L)
- OpenAlex
- W2886445457
- Cited by
- 292
- References
- 28
- FWCI
- 5.22
- Type
- journal article
- Language
- en
- Open access
- gold
- Funders
- Engineering and Physical Sciences Research Council (EP/M005976/1)
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- Pellegrini, E., Ballerini, L., Hernandez, M. D. C. V., Chappell, F. M., González‐Castro, V., Anblagan, D., Danso, S., Muñoz‐Maniega, S., Job, D., Pernet, C., Mair, G., MacGillivray, T. J., Trucco, E., & Wardlaw, J. M. (2018). Machine learning of neuroimaging for assisted diagnosis of cognitive impairment and dementia: A systematic review. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring. https://doi.org/10.1016/j.dadm.2018.07.004
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
