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

Journal: Brain and behavior
PMID: 40356536

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).

Authors

  • Mengdi Zhou
    Department of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
  • Huixin Li
    State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, Hong Kong SAR, China.
  • Xiaoxia Qu
    Department of Radiology, Beijing Tongren Hospital, Capital Medical University, No. 1 Dongjiaominxiang Street, Dongcheng District, Beijing, 100730, China; Clinical Center for Eye Tumors, Capital Medical University, Beijing, 100730, China.
  • Lirong Zhang
    School of Physical Science and Technology, Inner Mongolia University, Hohhot 010021, China.
  • Xueying He
    College of Intelligence and Information Engineering, Shandong University of Traditional Chinese Medicine, Jinan 250355, China.
  • Xiwen Wang
    Department of Radiology, the Second Affiliated Hospital of Nanchang University, Nanchang, China.
  • Jie Hong
  • Jing Fu
    Shaoxing Second Hospital, 123 Yanan Road, Shaoxing, Zhejiang 312000, PR China.
  • Zhaohui Liu
    Xi'an Institute of Optics and Precision Mechanics of CAS, Xi'an 710119, China.

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