E-resources
Peer reviewed
Open access
-
Borchert, Robin J.; Azevedo, Tiago; Badhwar, AmanPreet; Bernal, Jose; Betts, Matthew; Bruffaerts, Rose; Burkhart, Michael C.; Dewachter, Ilse; Gellersen, Helena M.; Low, Audrey; Lourida, Ilianna; Machado, Luiza; Madan, Christopher R.; Malpetti, Maura; Mejia, Jhony; Michopoulou, Sofia; Muñoz‐Neira, Carlos; Pepys, Jack; Peres, Marion; Phillips, Veronica; Ramanan, Siddharth; Tamburin, Stefano; Tantiangco, Hanz M.; Thakur, Lokendra; Tomassini, Alessandro; Vipin, Ashwati; Tang, Eugene; Newby, Danielle; Ranson, Janice M.; Llewellyn, David J.; Veldsman, Michele; Rittman, Timothy
Alzheimer's & dementia, December 2023, 2023-Dec, 2023-12-00, 20231201, Volume: 19, Issue: 12Journal Article
Introduction Artificial intelligence (AI) and neuroimaging offer new opportunities for diagnosis and prognosis of dementia. Methods We systematically reviewed studies reporting AI for neuroimaging in diagnosis and/or prognosis of cognitive neurodegenerative diseases. Results A total of 255 studies were identified. Most studies relied on the Alzheimer's Disease Neuroimaging Initiative dataset. Algorithmic classifiers were the most commonly used AI method (48%) and discriminative models performed best for differentiating Alzheimer's disease from controls. The accuracy of algorithms varied with the patient cohort, imaging modalities, and stratifiers used. Few studies performed validation in an independent cohort. Discussion The literature has several methodological limitations including lack of sufficient algorithm development descriptions and standard definitions. We make recommendations to improve model validation including addressing key clinical questions, providing sufficient description of AI methods and validating findings in independent datasets. Collaborative approaches between experts in AI and medicine will help achieve the promising potential of AI tools in practice. Highlights There has been a rapid expansion in the use of machine learning for diagnosis and prognosis in neurodegenerative disease Most studies (71%) relied on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with no other individual dataset used more than five times There has been a recent rise in the use of more complex discriminative models (e.g., neural networks) that performed better than other classifiers for classification of AD vs healthy controls We make recommendations to address methodological considerations, addressing key clinical questions, and validation We also make recommendations for the field more broadly to standardize outcome measures, address gaps in the literature, and monitor sources of bias
Author
![loading ... loading ...](themes/default/img/ajax-loading.gif)
Shelf entry
Permalink
- URL:
Impact factor
Access to the JCR database is permitted only to users from Slovenia. Your current IP address is not on the list of IP addresses with access permission, and authentication with the relevant AAI accout is required.
Year | Impact factor | Edition | Category | Classification | ||||
---|---|---|---|---|---|---|---|---|
JCR | SNIP | JCR | SNIP | JCR | SNIP | JCR | SNIP |
Select the library membership card:
If the library membership card is not in the list,
add a new one.
DRS, in which the journal is indexed
Database name | Field | Year |
---|
Links to authors' personal bibliographies | Links to information on researchers in the SICRIS system |
---|
Source: Personal bibliographies
and: SICRIS
The material is available in full text. If you wish to order the material anyway, click the Continue button.