自闭症谱系障碍的早期检测方法：从临床筛查到多模态人工智能
Early Detection Methods for Autism Spectrum Disorder: From Clinical Screening to Multimodal AI.
摘要(中文译文)
幼儿期孤独症谱系障碍（ASD）的早期识别对于及时转诊、发育监测和获得早期干预至关重要。然而，传统的筛查和诊断路径往往依赖于家长报告工具、阶段性临床观察以及由专科医生实施的评估，这可能延迟生命最初几年内的识别。本范围综述描绘了从传统临床筛查到多模态人工智能（AI）的早期ASD检测方法学图景。研究在主要生物医学、心理学和工程学数据库中进行了结构化文献检索，纳入2010年1月至2026年5月间发表的研究。经过筛选和资格评估，65篇证据来源被纳入定性综合，并辅以额外的方法学指南以支持报告和评价。所综述的证据表明，早期ASD检测正日益从单次临床评估转向多维风险特征描述。临床和行为筛查工具仍是早期识别的基础，而眼动追踪、基于视频的运动分析、声学和语音生物标志物、脑电图（EEG）、功能性近红外光谱（fNIRS）以及分子或基因组指标可在不同发育窗口提供互补信息。基于AI的方法，包括机器学习、深度学习、Transformer架构、多模态融合策略以及基于基础模型的表征学习，可能改善对注视、运动、发声、神经活动和生物学风险的客观量化。然而，大多数AI辅助系统仍受限于数据集规模小且异质性高、外部验证不足、人群偏倚、隐私问题、计算负担以及可解释性有限。本综述认为，未来的早期ASD检测系统应作为临床医生监督下的决策支持工具来开发，而非自主诊断工具。具有临床意义的进展将需要稳健的外部验证、隐私保护性部署、与年龄相适应的风险分层，以及本质上可解释的架构，使模型输出与发育和临床知识相一致。
Abstract
Early detection of autism spectrum disorder (ASD) in young children is essential for timely referral, developmental monitoring, and access to early intervention. However, conventional screening and diagnostic pathways often depend on parent-report instruments, episodic clinical observation, and specialist-administered assessments, which may delay identification during the first years of life. This scoping review maps the methodological landscape of early ASD detection from traditional clinical screening to multimodal artificial intelligence (AI). A structured literature search was conducted across major biomedical, psychological, and engineering databases for studies published between January 2010 and May 2026. After screening and eligibility assessment, 65 evidence sources were included in the qualitative synthesis, with additional methodological guidelines used to support reporting and appraisal. The reviewed evidence shows that early ASD detection is increasingly shifting from single-session clinical assessment toward multidimensional risk characterization. Clinical and behavioral screening tools remain the foundation of early identification, while eye tracking, video-based motor analysis, acoustic and vocal biomarkers, electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), and molecular or genomic indicators provide complementary information across different developmental windows. AI-based methods, including machine learning, deep learning, Transformer architectures, multimodal fusion strategies, and foundation-model-based representation learning, may improve the objective quantification of gaze, movement, vocalization, neural activity, and biological risk. Nevertheless, most AI-assisted systems remain limited by small and heterogeneous datasets, insufficient external validation, population bias, privacy concerns, computational burden, and limited interpretability. This review argues that future early ASD detection systems should be developed as clinician-supervised decision-support tools rather than autonomous diagnostic instruments. Clinically meaningful progress will require robust external validation, privacy-preserving deployment, age-appropriate risk stratification, and intrinsically interpretable architectures that align model outputs with developmental and clinical knowledge.
如何引用
AMA
Wenhao Luo, Zhiwu Yin, Jianbiao Dai. Early Detection Methods for Autism Spectrum Disorder: From Clinical Screening to Multimodal AI.. Diagnostics (Basel, Switzerland). 2026; doi:10.3390/diagnostics16152376.
APA
Wenhao Luo, Zhiwu Yin, Jianbiao Dai (2026). Early Detection Methods for Autism Spectrum Disorder: From Clinical Screening to Multimodal AI.. Diagnostics (Basel, Switzerland). https://doi.org/10.3390/diagnostics16152376
GB/T 7714
Wenhao Luo, Zhiwu Yin, Jianbiao Dai. Early Detection Methods for Autism Spectrum Disorder: From Clinical Screening to Multimodal AI.[J]. Diagnostics (Basel, Switzerland), 2026 doi:10.3390/diagnostics16152376.
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