Climent-Pérez Pau, Martínez-González Agustín Ernesto, Andreo-Martínez Pedro
Department of Computing Technology, University of Alicante, 03690 San Vicente del Raspeig, Alicante, Spain.
Department of Developmental Psychology and Didactics, University of Alicante, 03690 San Vicente del Raspeig, Alicante, Spain.
Children (Basel). 2024 Jul 31;11(8):931. doi: 10.3390/children11080931.
Autism spectrum disorder (ASD) is a highly heterogeneous neurodevelopmental disorder whose etiology is not known today, but everything indicates that it is multifactorial. For example, genetic and epigenetic factors seem to be involved in the etiology of ASD. In recent years, there has been an increase in studies on the implications of gut microbiota (GM) on the behavior of children with ASD given that dysbiosis in GM may trigger the onset, development and progression of ASD through the microbiota-gut-brain axis. At the same time, significant progress has occurred in the development of artificial intelligence (AI).
The aim of the present study was to perform a systematic review of articles using AI to analyze GM in individuals with ASD. In line with the PRISMA model, 12 articles using AI to analyze GM in ASD were selected.
Outcomes reveal that the majority of relevant studies on this topic have been conducted in China (33.3%) and Italy (25%), followed by the Netherlands (16.6%), Mexico (16.6%) and South Korea (8.3%).
The bacteria is the most relevant biomarker with regard to ASD. Although AI provides a very promising approach to data analysis, caution is needed to avoid the over-interpretation of preliminary findings. A first step must be taken to analyze GM in a representative general population and ASD samples in order to obtain a GM standard according to age, sex and country. Thus, more work is required to bridge the gap between AI in mental health research and clinical care in ASD.
自闭症谱系障碍（ASD）是一种高度异质性的神经发育障碍，其病因至今尚不明确，但一切迹象表明它是多因素的。例如，遗传和表观遗传因素似乎与ASD的病因有关。近年来，鉴于肠道微生物群（GM）失调可能通过微生物群-肠-脑轴触发ASD的发病、发展和进展，关于GM对ASD儿童行为影响的研究有所增加。与此同时，人工智能（AI）的发展取得了重大进展。
本研究的目的是对使用AI分析ASD个体GM的文章进行系统综述。根据PRISMA模型，选择了12篇使用AI分析ASD中GM的文章。
结果显示，关于该主题的大多数相关研究在中国（33.3%）和意大利（25%）进行，其次是荷兰（16.6%）、墨西哥（16.6%）和韩国（8.3%）。
就ASD而言，细菌是最相关的生物标志物。尽管AI为数据分析提供了一种非常有前景的方法，但需要谨慎避免对初步结果的过度解读。必须首先对具有代表性的普通人群和ASD样本中的GM进行分析，以便根据年龄、性别和国家获得GM标准。因此，需要做更多工作来弥合心理健康研究中的AI与ASD临床护理之间的差距。
