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学习算法:用于预测非酒精性脂肪肝炎的新选择.

Gang Li1, Tian-Lei Zheng2, Xiao-Ling Chi3

  • 1MAFLD Research Center, Department of Hepatology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.

Hepatobiliary surgery and nutrition
|August 21, 2023
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概括

一个新的机器学习算法, LEARN,使用基于阻抗的身体成分测量来非侵入性地识别非酒精性脂肪肝炎 (NASH). 这种自动化方法为脂肪肝疾病提供了更简单,更准确的诊断方法.

关键词:
非酒精性脂肪肝疾病 (NAFLD) 是一种非酒精性脂肪肝疾病.纳什 (LEARN) 算法的生物电阻分析身体构成 身体组成非酒精性脂肪肝炎 (NASH) 是一种非酒精性脂肪肝炎.

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科学领域:

  • 肝病学 肝病学是一种肝病学.
  • 医疗成像医学成像
  • 人工智能在医学中的应用

背景情况:

  • 非酒精性脂肪肝炎 (NASH) 诊断缺乏准确的非侵入性方法.
  • 基于阻抗的身体成分测量是可重复的,与非酒精性脂肪肝 (NAFLD) 严重程度相关.

研究的目的:

  • 开发一种全新的全自动机器学习算法,用于NASH识别.
  • 利用深度神经网络和基于阻抗的身体组成数据用于NASH诊断.

主要方法:

  • 开发了使用深度神经网络的纳什 (LEARN) 算法的生物电阻分析.
  • 从六个中国医疗中心对766名活检证明的NAFLD患者进行了训练和验证该算法.
  • 使用基于阻抗的身体组成,年龄,性别,高血压和糖尿病数据.

主要成果:

  • 学习算法准确地预测了具有良好的区分能力的NASH可能性 (AUROC 0.81在训练中,0.80在验证中).
  • 学习优于现有的非侵入性得分,如CK-18 M30,头发,离子和NICE (P < 0.001).
  • 该算法在患者子组和部分数据上表现出强大的性能.

结论:

  • 学习算法提供了一个简单的,自动化的,非侵入性的方法来识别NASH.
  • 这种方法解决了对准确的非侵入性NASH诊断的未满足需求.