Solek Purboyo, Nurfitri Eka, Sahril Indra, Prasetya Taufan, Rizqiamuti Anggia Farrah, Burhan Burhan, Rachmawati Irma, Gamayani Uni, Rusmil Kusnandi, Chandra Lukman Ade, Afriandi Irvan, Gunawan Kevin
Department of Child Health, Padjadjaran University Faculty of Medicine, Hasan Sadikin General Hospital, West Java, Indonesia.
Department of Neurology, Padjadjaran University Faculty of Medicine, Hasan Sadikin General Hospital, West Java, Indonesia.
Turk Arch Pediatr. 2025 Mar 3;60(2):126-140. doi: 10.5152/TurkArchPediatr.2025.24183.
Objective: Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by challenges in social communication and repetitive behaviors. This systematic review examines the application of artificial intelligence (AI) in diagnosing ASD, focusing on pediatric populations aged 0-18 years. Materials and methods: A systematic review was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines. Inclusion criteria encompassed studies applying AI techniques for ASD diagnosis, primarily evaluated using metriclike accuracy. Non-English articles and studies not focusing on diagnostic applications were excluded. The literature search covered PubMed, ScienceDirect, CENTRAL, ProQuest, Web of Science, and Google Scholar up to November 9, 2024. Bias assessment was performed using the Joanna Briggs Institute checklist for critical appraisal. Results: The review included 25 studies. These studies explored AI-driven approaches that demonstrated high accuracy in classifying ASD using various data modalities, including visual (facial, home videos, eye-tracking), motor function, behavioral, microbiome, genetic, and neuroimaging data. Key findings highlight the efficacy of AI in analyzing complex datasets, identifying subtle ASD markers, and potentially enabling earlier intervention. The studies showed improved diagnostic accuracy, reduced assessment time, and enhanced predictive capabilities. Conclusion: The integration of AI technologies in ASD diagnosis presents a promising frontier for enhancing diagnostic accuracy, efficiency, and early detection. While these tools can increase accessibility to ASD screening in underserved areas, challenges related to data quality, privacy, ethics, and clinical integration remain. Future research should focus on applying diverse AI techniques to large populations for comparative analysis to develop more robust diagnostic models.
自闭症谱系障碍(ASD)是一种复杂的神经发育疾病,其特征为社交沟通方面存在障碍以及有重复行为。本系统综述考察人工智能(AI)在ASD诊断中的应用,重点关注0至18岁的儿童群体。材料与方法:按照《系统综述与Meta分析优先报告条目2020》指南进行系统综述。纳入标准包括应用AI技术进行ASD诊断的研究,主要使用诸如准确率等指标进行评估。排除非英文文章以及未聚焦于诊断应用的研究。文献检索涵盖截至2024年11月9日的PubMed、ScienceDirect、CENTRAL、ProQuest、Web of Science和谷歌学术。使用乔安娜·布里格斯研究所的批判性评价清单进行偏倚评估。结果:该综述纳入了25项研究。这些研究探索了人工智能驱动的方法,这些方法在使用各种数据模式(包括视觉数据(面部、家庭视频、眼动追踪))、运动功能、行为、微生物组、基因和神经影像数据对ASD进行分类时显示出高准确率。主要发现突出了AI在分析复杂数据集、识别ASD细微标志物以及潜在实现早期干预方面的功效。这些研究显示出诊断准确率提高、评估时间缩短以及预测能力增强。结论:将AI技术整合到ASD诊断中为提高诊断准确率、效率和早期检测提供了一个有前景的领域。虽然这些工具可以增加在服务不足地区进行ASD筛查的可及性,但与数据质量、隐私、伦理和临床整合相关的挑战仍然存在。未来的研究应专注于将多样的AI技术应用于大量人群进行比较分析,以开发更强大的诊断模型。