Battista Petronilla, Salvatore Christian, Berlingeri Manuela, Cerasa Antonio, Castiglioni Isabella
Scientific Clinical Institutes Maugeri IRCCS, Institute of Bari, Pavia, Italy.
Department of Science, Technology and Society, Scuola Universitaria Superiore IUSS Pavia, Piazza della Vittoria 15, 27100 Pavia, Italy; DeepTrace Technologies S.r.l., Via Conservatorio 17, 20122 Milan, Italy.
Neurosci Biobehav Rev. 2020 Jul;114:211-228. doi: 10.1016/j.neubiorev.2020.04.026. Epub 2020 May 11.
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.
阿尔茨海默病（AD）领域当前面临的挑战之一是识别出将转化为AD的轻度认知障碍（MCI）患者。人工智能，尤其是机器学习（ML），已成为提取可靠预测指标并自动对不同AD表型进行分类的更强大方法之一。现在是时候加速将这些知识转化到临床实践中了，主要是通过使用源自神经心理学评估的低成本特征。我们进行了一项荟萃分析，以评估ML和神经心理学测量对MCI患者自动分类及其转化为AD的预测的贡献。通过定量双变量随机效应荟萃分析方法获得患者分类的合并敏感性和特异性。尽管观察到高度异质性，但荟萃分析结果表明，应用于神经心理学测量的ML可导致成功的自动分类，作为筛查工具比作为预后工具更具特异性。ML可以提取出能使分类准确性最大化的相关神经心理学测试类别。
