Machine Learning‐Based Identification of Children With Intermittent Exotropia Using Multiple Resting‐State Functional Magnetic Resonance Imaging Features

Machine Learning‐Based Identification of Children With Intermittent Exotropia Using Multiple Resting‐State Functional Magnetic Resonance Imaging Features

Abstract Objective To investigate the performance of machine learning (ML) methods based on resting‐state functional magnetic resonance imaging (rs‐fMRI) parameters in distinguishing children with intermittent exotropia (IXT) from healthy controls (HCs). Method Forty‐one IXT children and 36 HCs were...

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Bibliographic Details
Main Authors: Mengdi Zhou, Huixin Li, Xiaoxia Qu, Lirong Zhang, Xueying He, Xiwen Wang, Jie Hong, Jing Fu, Zhaohui Liu
Format: Article
Language:English
Published: Wiley 2025-05-01
Series:Brain and Behavior
Subjects:
Intermittent exotropia
machine learning
resting‐state functional magnetic resonance imaging
spontaneous activity
Online Access:https://doi.org/10.1002/brb3.70556
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https://doi.org/10.1002/brb3.70556

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