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Leveraging AI-Driven Neuroimaging Biomarkers for Early Detection and Social Function Prediction in Autism Spectrum Disorders: A Systematic Review.利用人工智能驱动的神经影像生物标志物进行自闭症谱系障碍的早期检测和社会功能预测：一项系统综述。
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本文引用的文献

1
Data-centric automated approach to predict autism spectrum disorder based on selective features and explainable artificial intelligence.基于选择性特征和可解释人工智能的以数据为中心的自闭症谱系障碍预测自动化方法。
Front Comput Neurosci. 2024 Oct 21;18:1489463. doi: 10.3389/fncom.2024.1489463. eCollection 2024.
2
Utilizing deep learning models in an intelligent eye-tracking system for autism spectrum disorder diagnosis.在用于自闭症谱系障碍诊断的智能眼动追踪系统中运用深度学习模型。
Front Med (Lausanne). 2024 Jul 19;11:1436646. doi: 10.3389/fmed.2024.1436646. eCollection 2024.
3
Learning Clusters in Autism Spectrum Disorder: Image-Based Clustering of Eye-Tracking Scanpaths with Deep Autoencoder.自闭症谱系障碍中的学习集群：基于深度自动编码器的眼动追踪扫描路径的图像聚类
Annu Int Conf IEEE Eng Med Biol Soc. 2019 Jul;2019:1417-1420. doi: 10.1109/EMBC.2019.8856904.

Editorial: Improving autism spectrum disorder diagnosis using machine learning techniques.

作者信息

Elbattah Mahmoud, Ali Sadek Ibrahim Osman, Dequen Gilles

机构信息

Laboratoire MIS, Université de Picardie Jules Verne, Amiens, France.

College of Art, Technology and Environment, University of the West of England, Bristol, United Kingdom.

出版信息

Front Neuroinform. 2024 Dec 6;18:1529839. doi: 10.3389/fninf.2024.1529839. eCollection 2024.

DOI:10.3389/fninf.2024.1529839
PMID:39712346
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11659280/
Abstract
摘要