Prediction of postoperative visual cognitive impairment using graph theory and machine learning based on resting-state brain networks.

Journal: BMC medical imaging
Published Date: Jul 30, 2025

Abstract

BACKGROUND: Visual cognitive impairment is among the most common postoperative cognitive dysfunctions, significantly impacting recovery and quality of life in elderly patients. However, effective preoperative prediction methods remain lacking. We developed a machine learning model using graph theory analysis of resting-state functional connectivity networks to predict postoperative visual cognitive impairment.

Authors

  • Songbin Liu
    Department of Anesthesiology, Huadong Hospital, Fudan University, Shanghai, 200040, China.
  • Zhaoshun Jiang
    Department of Anesthesiology, Huadong Hospital, Fudan University, Shanghai, 200040, China.
  • Pei Ye
    Department of Anesthesiology, Huadong Hospital, Fudan University, Shanghai, 200040, China.
  • Feidong Lv
    Department of Anesthesiology, Huadong Hospital, Fudan University, Shanghai, 200040, China.
  • Fei Tao
    State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.
  • Yuxi Cai
    Department of Anesthesiology, Huadong Hospital, Fudan University, Shanghai, 200040, China.
  • Lina Yi
    Department of Anesthesiology, Huadong Hospital, Fudan University, Shanghai, 200040, China.
  • Shihong Li
    Department of Radiology, Huadong Hospital, Fudan University, Shanghai, 200040, China.
  • Guoqing Wu
    Department of Electronic Engineering, Fudan University, Shanghai, China.
  • Jun Zhang
    First School of Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China.
  • Weidong Gu
    Department of Anesthesiology, Huadong Hospital, Fudan University, 200040, Shanghai, People's Republic of China. mcwgwd@163.com.

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