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一个可解释的深度学习模型用于价预测.

Shuang Ma1, Haifeng Wang1, Wei Zhao2

  • 1School of Information Science and Engineering, Linyi University, Linyi University, Linyi City, Shandong Province, Linyi, 276000, Linyi, China; Linyi People's Hospital Health and Medical Big Data Center, Linyi City, Shandong Province, Linyi, 276034, Linyi, China.

Computers in biology and medicine
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概括

本研究引入了一种可解释的深度学习模型,用于精确的价 (HV) 诊断,自动化地标识别和角度计算,以提高临床环境中的准确性和效率.

关键词:
在AG-UNet上使用.深度学习模型深度学习模型哈卢克斯瓦尔古斯 (Hallux valgus) 是一种气体.双脚间角的角度是什么意思标志性的地标 标志性的地标在SE-DNN网络网络中,一些X射线图像.

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科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 整形外科 整形外科 整形外科

背景情况:

  • 状 (HV) 诊断依赖于手动测量状角 (HVA) 和间角 (IMA),这耗时且容易出现错误.
  • 需要自动化方法来提高HV诊断的效率和准确性.

研究的目的:

  • 开发一个可解释的深度学习模型,用于自动注释12英尺的地标.
  • 为了自动计算HVA和IMA用于哈卢克斯瓦尔古斯诊断.
  • 为了提高华氏体的诊断的效率和准确性.

主要方法:

  • 一个深度学习模型 (SE-DNN) 在2000个手动标记的足部X射线图像上受过训练.
  • 使用AG-UNet架构对关键脚结构 (PH1,MT1,MT2) 进行细分.
  • 模型的性能通过将其地标识别和角度计算与手术专家的手动测量进行比较来评估.

主要成果:

  • 该模型实现了1.9毫米至5.6毫米之间的平均基准误差距,整体平均误差小于3.1毫米.
  • 模型和专家之间对HVA和IMA测量的评价者间一致性很高,类内相关系数 (ICC) 结果≥0.9.
  • 可解释的深度学习模型的可靠性和准确性与手工专家测量相当或超过.

结论:

  • 成功开发了一种可解释的深度学习模型,用于自动测试价的诊断.
  • 该模型准确地识别了12个地标,并计算了HVA和IMA,在可靠性和准确性方面比手工方法具有显著的优势.
  • 开发的方法已在医院成功实施,显示出显著的检测结果.