Valladares-Rodriguez Sonia, Pérez-Rodriguez Roberto, Fernandez-Iglesias J Manuel, Anido-Rifón Luis E, Facal David, Rivas-Costa Carlos
Methods Inf Med. 2018 Sep;57(4):197-207. doi: 10.3414/ME17-02-0011. Epub 2018 Sep 24.
Alzheimer's disease (AD) is one of the most prevalent diseases among the adult population. The early detection of Mild Cognitive Impairment (MCI), which may trigger AD, is essential to slow down the cognitive decline process.
This paper presents a suit of serious games that aims at detecting AD and MCI overcoming the limitations of traditional tests, as they are time-consuming, affected by confounding factors that distort the result and usually administered when symptoms are evident and it is too late for preventive measures. The battery, named Panoramix, assesses the main early cognitive markers (i.e., memory, executive functions, attention and gnosias). Regarding its validation, it has been tested with a cohort study of 16 seniors, including AD, MCI and healthy individuals.
This first pilot study offered initial evidence about psychometric validity, and more specifically about construct, criterion and external validity. After an analysis using machine learning techniques, findings show a promising 100% rate of success in classification abilities using a subset of three games in the battery. Thus, results are encouraging as all healthy subjects were correctly discriminated from those already suffering AD or MCI.
The solid potential of digital serious games and machine learning for the early detection of dementia processes is demonstrated. Such a promising performance encourages further research to eventually introduce this technique for the clinical diagnosis of cognitive impairment.
阿尔茨海默病（AD）是成年人群中最常见的疾病之一。早期发现可能引发AD的轻度认知障碍（MCI）对于减缓认知衰退进程至关重要。
本文介绍了一套严肃游戏，旨在检测AD和MCI，克服传统测试的局限性，因为传统测试耗时、受混淆因素影响而使结果失真，且通常在症状明显时进行，此时采取预防措施已为时过晚。这套名为Panoramix的测试组合评估主要的早期认知指标（即记忆、执行功能、注意力和认知）。关于其验证，已在一项对16名老年人的队列研究中进行了测试，包括AD患者、MCI患者和健康个体。
这项初步的试点研究提供了关于心理测量效度的初步证据，更具体地说是关于结构效度、标准效度和外部效度的证据。在使用机器学习技术进行分析后，研究结果显示，使用测试组合中的三款游戏子集进行分类时，成功率有望达到100%。因此，结果令人鼓舞，因为所有健康受试者都能与已患AD或MCI的受试者正确区分开来。
数字严肃游戏和机器学习在早期检测痴呆症进程方面具有坚实的潜力。如此有前景的表现鼓励进一步研究，最终将这项技术引入认知障碍的临床诊断。
