AI 中文总结
本系统综述分析2017-2023年55项研究，探究ML在ASD诊断治疗的应用，指出监督学习为主、深度学习作用扩大，需整合多模态数据并加强跨学科合作。
AI 中文摘要
自闭症谱系障碍（ASD）是一种发育障碍，特征为社交互动与沟通方面存在挑战。由于ASD的病因仍不明确，识别相关特征与隐藏关联对早期诊断至关重要。本系统综述评估了2017年至2023年间55项关于机器学习（ML）技术应用于ASD的研究，主要目标是探究ASD研究中近期的ML应用，明确可改善诊断与治疗的趋势、技术及数据集。监督学习方法占据主导地位，因其与ASD诊断需求契合度高；不过，随着数据可得性提升，深度学习的作用正在扩大。包含无监督学习、深度学习及模糊逻辑的混合方法这类新兴技术，未来发展值得关注。本综述强调了关键挑战与机遇，尤其是需要能整合遗传、临床等复杂数据的模型，以提升诊断准确率与治疗效果。此外，纳入可穿戴设备、生物传感器等创新数据源，有望实现连续且非侵入式的监测，从而更全面地理解ASD。研究结果表明，应对当前挑战需要跨学科合作及针对ASD的扩充数据集；未来的ML模型将得益于更广泛的多模态数据整合，助力研究人员更全面地应对ASD的复杂性。
英文摘要
Autism spectrum disorder (ASD) is a developmental disability characterized by challenges in social interaction and communication. As the causes of ASD remain unclear, identifying relevant features and hidden correlations is crucial for early diagnosis. This systematic review evaluates 55 studies from 2017 to 2023 on the application of machine learning (ML) techniques to ASD. The primary objective is to examine recent ML applications in ASD research, identifying trends, techniques, and datasets that enhance diagnosis and treatment. Supervised learning methods dominate, as they align well with ASD diagnostic needs; however, the role of deep learning is expanding with greater data availability. Emerging techniques based on hybrid methods, where unsupervised, deep learning, and fuzzy logic could be included, will be interesting to observe in the future. The review highlights key challenges and opportunities, particularly the need for models that can integrate complex data -such as genetic and clinical information- to improve diagnostic accuracy and treatment outcomes. Additionally, incorporating innovative data sources, like wearable devices and biometric sensors, could enable continuous and non-intrusive monitoring, providing a more holistic understanding of ASD. Findings emphasize that addressing current challenges requires interdisciplinary collaboration and expanded datasets tailored to ASD. Future ML models will benefit from broader multimodal data integration, enabling researchers to more comprehensively address the complexities of ASD.
Comments17 pages, 8 figures
Journal refA systematic review of machine learning techniques to address diagnosis and treatment of autism: challenges and opportunitie. Heliyon 12(1), e44359, 2026
DOI：10.1016/j.heliyon.2025.e44359
