Machine learning of neuroimaging for assisted diagnosis of cognitive impairment and dementia: A systematic review

Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring

Victor González‐Castro¹  Samuel Danso²  Susana Muñoz‐Maniega²  Enrico Pellegrini²  Tom J. MacGillivray²  Francesca M. Chappell²  Cyril Pernet²  Devasuda Anblagan²  Dominic Job²  Lucia Ballerini²  Grant Mair²  Maria del C. Valdes Hernandez²  Joanna M. Wardlaw²  Emanuele Trucco³ 

[1] Department of Electrical, Systems and Automatics EngineeringUniversidad de LeónLeónSpain;Division of Neuroimaging, Centre for Clinical Brain Sciences and Edinburgh ImagingUniversity of EdinburghScotlandUK;VAMPIRE project, Computing, School of Science and Engineering, University of DundeeDundeeUK;

关键词: Dementia;    Cerebrovascular disease;    Pathological aging;    Small vessel disease;    MRI;    Machine learning;   

DOI  :  10.1016/j.dadm.2018.07.004

来源: DOAJ

【 摘 要 】

Abstract 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.

【 授权许可】

Unknown