在放射学时间序列中使用深度学习衍生图像特征来进行个性化预测:结肠传输数据中的概念证明
Brendan S Kelly1,2,3, Prateek Mathur4, Jan Plesniar5
1Department of Radiology, St Vincent's University Hospital, Dublin, Ireland. brendanskelly@me.com.
European radiology
|June 7, 2023
概括
罗神经网络在结肠传输研究 (CTS) 中准确地识别了放射性不透明珠子. 这些网络在时间序列模型中使用时,可以比传统的统计方法更准确地预测患者在研究中的进展.
科学领域:
- 医疗成像中的人工智能
- 放射学和诊断成像 放射学和诊断成像
- 机器学习用于时间序列分析.
背景情况:
- 结肠过渡时间研究 (CTS) 对于评估胃肠机动性至关重要.
- 在CTS中对串行放射图的传统分析可能是劳动密集型和主观的.
- 计算机视觉和深度学习方面的进步为自动化和改进放射性评估提供了潜力.
研究的目的:
- 通过使用罗神经网络 (SNN) 在CTS图像中对放射性不透明珠的存在和数量进行分类.
- 将SNN衍生特征集成到时间序列模型中,用于预测通过CTS的患者进展.
- 将这种新型深度学习方法的预测准确性与传统的统计方法进行比较.
主要方法:
- 对来自229名患者的568张CTS图像 (2010-2020年) 的回顾性分析.
- 深度学习模型的培训和测试,特别是SNN架构,用于图像分类和特征提取.
- 使用SNN输出作为高斯过程回归器 (GPR) 的输入来预测总研究持续时间.
主要成果:
- 性能最好的SNN模型实现了高精度 (0.988),精度 (0.986) 和回忆 (1.0) 的珠子分类.
- 采用SNN特征的GPR模型显著超过了只有珠数和指数曲线拟合的GPR模型.
- 基于SNN的时间序列模型实现了0.9天的平均绝对误差 (MAE),而其他方法的平均误差为2.3和6.3天 (p <0.05).
结论:
- 罗神经网络在识别CTS的放射性不透明珠子方面表现强.
- 开发的放射性时间序列模型,利用SNN,可以比统计模型更好地预测CTS进展.
- 这种方法可以实现更准确,个性化的预测,并且在各种医疗领域的变化评估中具有潜在的临床应用.
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