Détail du document
Identifiant
doi:10.1186/s12883-023-03323-2...
Auteur
Ahmadzadeh, Maryam Christie, Gregory J. Cosco, Theodore D. Arab, Ali Mansouri, Mehrdad Wagner, Kevin R. DiPaola, Steve Moreno, SylvainLangue
enEditeur
BioMed Central
Catégorie
Medicine & Public Health
Année
2023
Date de référencement
23/08/2023
Mots clés
systematic review alzheimer mild cognitive impairment prediction neuroimaging machine learning neuroimaging systematicRésumé
Background This systematic review synthesizes the most recent neuroimaging procedures and machine learning approaches for the prediction of conversion from mild cognitive impairment to Alzheimer’s disease dementia.
Methods We systematically searched PubMed, SCOPUS, and Web of Science databases following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) systematic review guidelines.
Results Our search returned 2572 articles, 56 of which met the criteria for inclusion in the final selection.
The multimodality framework and deep learning techniques showed potential for predicting the conversion of MCI to AD dementia.
Conclusion Findings of this systematic review identified that the possibility of using neuroimaging data processed by advanced learning algorithms is promising for the prediction of AD progression.
We also provided a detailed description of the challenges that researchers are faced along with future research directions.
The protocol has been registered in the International Prospective Register of Systematic Reviews– CRD42019133402 and published in the Systematic Reviews journal.
Ahmadzadeh, Maryam,Christie, Gregory J.,Cosco, Theodore D.,Arab, Ali,Mansouri, Mehrdad,Wagner, Kevin R.,DiPaola, Steve,Moreno, Sylvain, 2023, Neuroimaging and machine learning for studying the pathways from mild cognitive impairment to alzheimer’s disease: a systematic review, BioMed Central
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