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机器学习算法和人工神经网络用于使用轨道参数预测精神分裂症.

Elif Emre1, Derya Ozturk Soylemez2, Yusuf Secgin3

  • 1Department of Anatomy, Faculty of Medicine, Firat University, Elazig, Turkey.

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|November 29, 2025
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概括

机器学习和人工神经网络通过分析CT扫描的轨道测量来诊断精神分裂症. 这种人工智能驱动的方法可以为早期发现这种持久性精神疾病提供新的工具.

关键词:
人工神经网络的人工神经网络计算机断层扫描 (CT) 是一种计算机断层扫描.机器学习是机器学习.轨道参数 轨道参数精神分裂症是一种精神分裂症.

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科学领域:

  • 神经成像是一种神经成像.
  • 精神病学中的人工智能
  • 医学诊断 医学诊断 医学诊断

背景情况:

  • 精神分裂症是一种复杂的精神疾病,其起源不清楚.
  • 当前的诊断方法可能是漫长和主观的.
  • 研究精神分裂症的新生物标志物至关重要.

研究的目的:

  • 探索使用计算机断层扫描 (CT) 扫描轨道来诊断精神分裂症的潜力.
  • 应用人工神经网络 (ANN) 和机器学习 (ML) 算法用于基于轨道形态测量的精神分裂症检测.

主要方法:

  • 对180名个人CT扫描的回顾性分析 (90名健康人群,90名精神分裂症患者).
  • 测量各种轨道和骨参数,包括轨道宽度,光圈面积和视神经外宽度.
  • 应用ML算法 (例如,额外树分类器) 和ANN (例如,多层感知子分类器) 进行分类.

主要成果:

  • 在健康个体和精神分裂症患者之间,在轨道和头骨测量方面发现了显著的差异.
  • 额外树分类器获得了最高准确率 (0.78),多层感知子分类器获得了0.75.
  • 左轨道宽度被确定为使用SHAP分析仪进行诊断的最有影响力的特征.

结论:

  • 分析轨道形态测量的AI模型显示出潜力作为精神分裂症的诊断工具.
  • 从CT扫描中获得的轨道测量可以作为精神分裂症的客观生物标志物.
  • 这项研究强调了机器学习在精神病诊断中日益增长的作用.