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Neuroimaging computer‐aided diagnosis systems for Alzheimer's disease
Author(s) -
Karami Vania,
Nittari Giulio,
Amenta Francesco
Publication year - 2019
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.22300
Subject(s) - computer science , neuroimaging , computer aided , computer aided diagnosis , support vector machine , artificial intelligence , disease , identification (biology) , principal component analysis , medical physics , parametric statistics , machine learning , medicine , pathology , psychiatry , statistics , botany , biology , programming language , mathematics
This paper has reviewed the state‐of‐the‐art approaches for Computer Aided Diagnosis Systems (CADS) for Alzheimer's Disease (AD) using neuroimaging. Identification of the current approaches leads to improving the efficiency of these techniques. The analysis covered 110 articles published between 2009 and January 2018. Papers were chosen according to the Newcastle‐Ottawa criteria. MeSH terms were “computer aided diagnosis systems for Alzheimer's disease” and “computer aided diagnosis systems methods for diagnosis of Alzheimer's disease”. CADS algorithms have been presented with specific methods. There is no standardized approach to determine the best one. This study has tables that aimed to conclude all methods in a precise way. Among them, Statistical Parametric Mapping (SPM), Principal Component Analysis (PCA), and Support Vector Machine (SVM) were the most common, respectively. CADS for AD could become important in clinical practice in the near future. The evaluation criteria approved their efficiency as a second opinion besides the neurologist.