自动骨成熟度分级从骨盆X射线图通过深度学习为青少年异常发病性脊椎病变
Yang Zhao1, Junhua Zhang2, Hongjian Li3
1School of Information, Yunnan University, East Outer Ring South Road, Kunming, 650504, China.
Medical & biological engineering & computing
|January 21, 2025
概括
本研究引入了一种AI模型,用于使用Risser阶段评估青少年异常学脊椎病 (AIS). 自动化方法提高了确定骨成熟度的准确性,有助于临床评估.
科学领域:
- 整形外科和康复治疗
- 人工智能在医学中的应用
- 医学成像分析 医学成像分析
背景情况:
- 青少年无形脊柱脊椎病 (AIS) 是一种复杂的脊柱形,需要精确的骨成熟度评估.
- 瑞塞阶段对于AIS评估和治疗计划至关重要.
- 手动Risser阶段评估存在显著的观察者内部和观察者之间的变化,影响精度.
研究的目的:
- 开发和评估一种自动化的多任务学习方法,用于在AIS中精确的瑞塞阶段评估.
- 与现有方法相比,提高瑞塞阶段测定的准确性.
主要方法:
- 设计了一个多任务学习框架,包括一个改进的Swin变压器.
- 该模型提取了腹腔区域,并使用空间和通道重建卷积Swin块进行Risser阶段评估.
- 为了模型的可解释性,使用了Grad-CAM可视化.
主要成果:
- 拟议的方法在Risser阶段评估中实现了总体准确度为81.53%.
- 这种精度超过了包括ResNet50,ResNet101,Uni-former,Next-ViT,ConvNeXt和原来的Swin变压器在内的已知模型.
- 该模型通过Grad-CAM分析展示了强大的性能和可解释性.
结论:
- 开发的自动化瑞塞阶段评估方法为手动评估提供了精确可靠的替代方案.
- 这种人工智能驱动的方法有可能显著有利于青少年特异性脊柱病的临床管理.
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