Gkintoni Evgenia, Panagioti Maria, Vassilopoulos Stephanos P, Nikolaou Georgios, Boutsinas Basilis, Vantarakis Apostolos
Department of Educational Sciences and Social Work, University of Patras, 26504 Patras, Greece.
Division of Population Health, Health Services Research & Primary Care (LS), University of Manchester, Manchester M13 9PL, UK.
Healthcare (Basel). 2025 Jul 22;13(15):1776. doi: 10.3390/healthcare13151776.
: This systematic review examines artificial intelligence (AI) applications in neuroimaging for autism spectrum disorder (ASD), addressing six research questions regarding biomarker optimization, modality integration, social function prediction, developmental trajectories, clinical translation challenges, and multimodal data enhancement for earlier detection and improved outcomes. : Following PRISMA guidelines, we conducted a comprehensive literature search across 8 databases, yielding 146 studies from an initial 1872 records. These studies were systematically analyzed to address key questions regarding AI neuroimaging approaches in ASD detection and prognosis. : Neuroimaging combined with AI algorithms demonstrated significant potential for early ASD detection, with electroencephalography (EEG) showing promise. Machine learning classifiers achieved high diagnostic accuracy (85-99%) using features derived from neural oscillatory patterns, connectivity measures, and signal complexity metrics. Studies of infant populations have identified the 9-12-month developmental window as critical for biomarker detection and the onset of behavioral symptoms. Multimodal approaches that integrate various imaging techniques have substantially enhanced predictive capabilities, while longitudinal analyses have shown potential for tracking developmental trajectories and treatment responses. : AI-driven neuroimaging biomarkers represent a promising frontier in ASD research, potentially enabling the detection of symptoms before they manifest behaviorally and providing objective measures of intervention efficacy. While technical and methodological challenges remain, advancements in standardization, diverse sampling, and clinical validation could facilitate the translation of findings into practice, ultimately supporting earlier intervention during critical developmental periods and improving outcomes for individuals with ASD. Future research should prioritize large-scale validation studies and standardized protocols to realize the full potential of precision medicine in ASD.
本系统综述探讨了人工智能（AI）在自闭症谱系障碍（ASD）神经影像学中的应用，针对生物标志物优化、模态整合、社会功能预测、发育轨迹、临床转化挑战以及多模态数据增强以实现早期检测和改善预后等六个研究问题进行了探讨。遵循PRISMA指南，我们对8个数据库进行了全面的文献检索，从最初的1872条记录中筛选出146项研究。对这些研究进行了系统分析，以解决有关AI神经影像学方法在ASD检测和预后方面的关键问题。神经影像学与AI算法相结合在ASD早期检测中显示出巨大潜力，脑电图（EEG）表现出前景。机器学习分类器利用从神经振荡模式、连接性测量和信号复杂性指标中提取的特征，实现了较高的诊断准确率（85-99%）。对婴儿群体的研究已确定9-12个月的发育窗口对于生物标志物检测和行为症状的出现至关重要。整合各种成像技术的多模态方法显著增强了预测能力，而纵向分析显示了跟踪发育轨迹和治疗反应的潜力。AI驱动的神经影像学生物标志物是ASD研究中一个有前景的前沿领域，有可能在症状出现之前进行检测，并提供干预效果的客观测量。虽然技术和方法挑战仍然存在，但标准化、多样化采样和临床验证方面的进展可以促进研究结果转化为实践，最终支持在关键发育阶段进行早期干预，并改善ASD患者的预后。未来的研究应优先进行大规模验证研究和标准化方案，以充分发挥精准医学在ASD中的潜力。
