One of the current challenges in the field of Alzheimer's disease (AD) is to identify patients with mild cognitive impairment (MCI) that will convert to AD. Artificial intelligence, in particular machine learning (ML), has established as one of more powerful approach to extract reliable predictors and to automatically classify different AD phenotypes. It is time to accelerate the translation of this knowledge in clinical practice, mainly by using low-cost features originating from the neuropsychological assessment. We performed a meta-analysis to assess the contribution of ML and neuropsychological measures for the automated classification of MCI patients and the prediction of their conversion to AD. The pooled sensitivity and specificity of patients’ classifications was obtained by means of a quantitative bivariate random-effect meta-analytic approach. Although a high heterogeneity was observed, the results of meta-analysis show that ML applied to neuropsychological measures can lead to a successful automatic classification, being more specific as screening rather than prognosis tool. Relevant categories of neuropsychological tests can be extracted by ML that maximize the classification accuracy. © 2020 Elsevier Ltd

Artificial intelligence and neuropsychological measures: The case of Alzheimer's disease

Battista P.;Salvatore C.;
2020

Abstract

One of the current challenges in the field of Alzheimer's disease (AD) is to identify patients with mild cognitive impairment (MCI) that will convert to AD. Artificial intelligence, in particular machine learning (ML), has established as one of more powerful approach to extract reliable predictors and to automatically classify different AD phenotypes. It is time to accelerate the translation of this knowledge in clinical practice, mainly by using low-cost features originating from the neuropsychological assessment. We performed a meta-analysis to assess the contribution of ML and neuropsychological measures for the automated classification of MCI patients and the prediction of their conversion to AD. The pooled sensitivity and specificity of patients’ classifications was obtained by means of a quantitative bivariate random-effect meta-analytic approach. Although a high heterogeneity was observed, the results of meta-analysis show that ML applied to neuropsychological measures can lead to a successful automatic classification, being more specific as screening rather than prognosis tool. Relevant categories of neuropsychological tests can be extracted by ML that maximize the classification accuracy. © 2020 Elsevier Ltd
Alzheimer disease
artificial intelligence
artificial neural network
classification algorithm
disease classification
early diagnosis
human
machine learning
mild cognitive impairment
neuroimaging
neuropsychological test
normal distribution
patient selection
priority journal
prognosis
Review
support vector machine
systematic review
AD
Automatic classification
Biomarkers
Cognitive measures
Machine learning
MCI
Mild cognitive impairment
Neurodegenerative diseases: dementia
Neuropsychological tests
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.12076/8259
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