A systematic review of machine learning techniques to address diagnosis and treatment of autism: challenges and opportunities
A systematic review of machine learning techniques to address diagnosis and treatment of autism: challenges and opportunities
针对自闭症诊断与治疗的机器学习技术系统综述：挑战与机遇
Abstract: 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.
摘要： 自闭症谱系障碍（ASD）是一种以社交互动和沟通障碍为特征的发育性残疾。由于 ASD 的病因尚不明确，识别相关特征和潜在关联对于早期诊断至关重要。本系统综述评估了 2017 年至 2023 年间关于机器学习（ML）技术在 ASD 应用方面的 55 项研究。
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.
其主要目标是审视近期机器学习在 ASD 研究中的应用，识别能够改善诊断和治疗的趋势、技术及数据集。监督学习方法目前占据主导地位，因为它们与 ASD 的诊断需求高度契合；然而，随着数据可用性的提高，深度学习的作用正在不断扩大。未来，基于混合方法（可能结合无监督学习、深度学习和模糊逻辑）的新兴技术将值得关注。
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.
该综述强调了关键的挑战与机遇，特别是开发能够整合复杂数据（如遗传和临床信息）以提高诊断准确性和治疗效果的模型的需求。此外，引入创新的数据源（如可穿戴设备和生物识别传感器）可以实现持续且非侵入式的监测，从而提供对 ASD 更全面的理解。
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.
研究结果强调，应对当前的挑战需要跨学科合作以及针对 ASD 量身定制的扩展数据集。未来的机器学习模型将受益于更广泛的多模态数据整合，使研究人员能够更全面地解决 ASD 的复杂性问题。
Journal reference: A systematic review of machine learning techniques to address diagnosis and treatment of autism: challenges and opportunities. Heliyon 12(1), e44359, 2026. 期刊参考： 针对自闭症诊断与治疗的机器学习技术系统综述：挑战与机遇。《Heliyon》12(1), e44359, 2026。