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从使用深度学习的头骨X射线图像估计婴儿年龄.

Heui Seung Lee1,2, Jaewoong Kang3, So Eui Kim3

  • 1Department of Neurosurgery, College of Medicine, Hallym University Sacred Heart Hospital, Hallym University, 22, Gwanpyeong-Ro 170Beon-Gil, Dongan-Gu, Anyang-Si, Gyeonggi-Do, 14068, Republic of Korea. antanatia@gmail.com.

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概括

深度学习模型使用头骨X射线准确预测婴儿的出生后年龄,有助于评估头骨发育. 这种非侵入性方法对临床诊断和发育评估具有前景.

关键词:
头骨突发症是什么 头骨突发症是什么婴儿年龄 婴儿年龄一个婴儿的头骨.头骨 suture 头骨 suture 头骨 suture 头骨 suture 头骨 suture 头骨 suture 头骨 suture 头骨 suture 头骨 suture这是X射线.

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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
  • 儿科 儿科 儿科

背景情况:

  • 准确估计产后年龄对于婴儿发育评估至关重要.
  • 传统的年龄估计方法可能具有侵入性或不太精确.
  • 头骨放射提供了一个对婴儿头骨发育的非侵入性窗口.

研究的目的:

  • 开发和评估深度学习模型,从头骨放射图中预测婴儿产后年龄.
  • 评估使用头骨X射线图像特征来评估头骨发育的可行性.
  • 使用可解释的AI技术,识别暗示头骨发育的关键放射特征.

主要方法:

  • 卷积神经网络 (CNN) 的模型,DenseNet-121和EfficientNet-v2-M,在1343名婴儿的4933张头骨X射线图像上进行了训练.
  • 这些模型以±1个月的误差预测了产后年龄.
  • 梯度加权类激活映射 (Grad-CAM) 用于可视化放射图中的关键区分区域.

主要成果:

  • EfficientNet-v2-M在侧面头骨视图 (平均85.1%±2.5%) 和前后 (AP) 视图 (平均77.0%±2.3%) 中的最大校正精度为87.3%,达到79.1%.
  • 在DenseNet-121中,横向视图的最大校正精度为84.2% (平均81.1%±2.9%) 和AP视图的最大校正精度为79.4% (平均78.0%±1.5%).
  • Saliency 地图突出 sutures (冠状,形,形,羊形) 和皮质骨密度作为头骨发育的关键指标.

结论:

  • 深度学习模型在预测婴儿出生后年龄方面表现出高准确度,从头骨放射图来看.
  • 这项研究验证了用于评估头骨发育的非侵入性放射特征的使用.
  • 这些发现支持人工智能工具的潜力,用于增强儿科临床诊断和发育监测.