使用深度神经网络与基于轴的注释自动估计角
Ryutaro Takeda1, Hiroyasu Mizuhara1, Akihiro Uchio1
1Department of Orthopaedic Surgery, Faculty of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8655, Japan.
Skeletal radiology
|March 13, 2024
概括
一个深度神经网络 (DNN) 模型准确地测量了足部放射图上的角 (HVA) 和间角 (IMA). 这种人工智能工具在这些关键的骨科测量中显示了与专家外科医生可比的准确性.
科学领域:
- 整形外科成像分析分析
- 医学中的人工智能
- 放射性测量技术的使用.
背景情况:
- 角 (HVA) 和间角 (IMA) 是诊断脚形的关键指标.
- 在X光照上手动测量HVA和IMA可能是主观的,耗时的.
- 开发用于精确放射分析的自动化方法对于高效的临床实践至关重要.
研究的目的:
- 开发和验证深度神经网络 (DNN) 模型,用于自动测量HVA和IMA.
- 通过将其测量结果与经验丰富的足外科医生的测量结果进行比较,评估DNN模型的准确性.
- 评估模型的性能与已建立的评级者之间的可靠性标准相比.
主要方法:
- 一个DNN模型被训练来识别足部X射线图上的骨轴,用于HVA和IMA计算.
- 该模型是使用来自联合队列的1798张放射图开发的.
- 对92张X射线图进行了追溯验证,将DNN测量与三个外科医生手工测量的中位数进行了比较.
主要成果:
- 在DNN模型中,HVA的平均绝对误差 (MAE) 为1.3°,远远低于外科医生的评分间差异 (2.0°).
- 对于IMA,模型的MAE为0.8°,与外科医生的评分间差异 (1.0°) 相比,没有显著差异.
- 该模型在测量HVA和IMA时表现出高精度和可靠性.
结论:
- 开发的DNN模型准确地测量了HVA和IMA在足部放射图上.
- 该模型的准确性与专业脚外科医生的准确性相当.
- 这种自动化方法提供了一个可靠的替代方法,用于对脚对齐的放射评估.
相关概念视频
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