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.

中文翻译：

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人工智能和神经心理学措施：阿尔茨海默氏病病例。

阿尔茨海默氏病（AD）领域当前的挑战之一是确定患有轻度认知障碍（MCI）并会转变为AD的患者。人工智能，尤其是机器学习（ML），已被确立为一种提取可靠预测因子并自动分类不同AD表型的更强大方法之一。现在是时候主要通过使用源自神经心理学评估的低成本功能，来加快临床实践中知识的翻译速度。我们进行了荟萃分析，以评估ML和神经心理学措施对MCI患者的自动分类及其对AD转化的预测的贡献。病人的综合敏感性和特异性 通过定量双变量随机效应荟萃分析方法获得分类。尽管观察到高度异质性，但荟萃分析的结果表明，将ML应用于神经心理学措施可以成功完成自动分类，将其更具体地用作筛查而非预后工具。ML可以提取神经心理学测试的相关类别，从而最大程度地提高分类准确性。