Autism spectrum disorder (ASD) is a neurodevelopmental incurable disorder with a long diagnostic period encountered in the early years of life. If diagnosed early, the negative effects of this disease can be reduced by starting special education early. Machine learning (ML), an increasingly ubiquitous technology, can be applied for the early diagnosis of ASD. The aim of this study is to examine and provide a comprehensive state-of-the-art review of ML research for the diagnosis of ASD based on (a) structural magnetic resonance image (MRI), (b) functional MRI and (c) hybrid imaging techniques over the past decade. The accuracy of the studies with a large number of participants is in general lower than those with fewer participants leading to the conclusion that further large-scale studies are needed. An examination of the age of the participants shows that the accuracy of the automated diagnosis of ASD is higher at a younger age range. ML technology is expected to contribute significantly to the early and rapid diagnosis of ASD in the coming years and become available to clinicians in the near future. This review is aimed to facilitate that.

中文翻译：

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使用脑成像进行机器学习 (ML) 诊断自闭症谱系障碍 (ASD)。


自闭症谱系障碍（ASD）是一种神经发育无法治愈的疾病，在生命的早期阶段会遇到较长的诊断期。如果早期诊断，可以通过早期开始特殊教育来减少这种疾病的负面影响。机器学习（ML）是一种日益普遍的技术，可应用于 ASD 的早期诊断。本研究的目的是基于 (a) 结构磁共振图像 (MRI)、(b) 功能 MRI 和 (c) 诊断 ASD 的 ML 研究，并对其进行全面的最新综述过去十年的混合成像技术。大量参与者的研究的准确性通常低于参与者较少的研究，从而得出需要进一步进行大规模研究的结论。对参与者年龄的检查表明，自闭症谱系障碍自动诊断的准确性在较小的年龄范围内更高。机器学习技术预计将在未来几年为 ASD 的早期和快速诊断做出重大贡献，并在不久的将来可供临床医生使用。本次审查旨在促进这一点。