一种使用集体模型的深度学习方法,自动创建基于图像的关节骨折注册表
Jacobien H F Oosterhoff1,2, Soomin Jeon1,3, Bardiya Akhbari1
1Department of Orthopaedic Surgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA.
OTA international : the open access journal of orthopaedic trauma
|December 28, 2023
概括
一个新的深度学习 (DL) 模型准确地从X光片中识别骨折,自动创建注册表. 这种方法可以加快骨折标记的速度,并提高老年患者的数据准确性.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 部骨折是老年人的重大健康问题,全球发病率正在上升.
- 现有的骨折注册表通常依赖于计费代码,导致报告不足和不准确.
- 需要更有效,更准确的方法来识别骨折和创建注册表.
研究的目的:
- 开发和评估一种基于深度学习 (DL) 的方法,用于自动创建关节骨折注册表.
- 评估DL在从放射图像中检测部骨折的准确性和效率.
- 为骨骨折患者群体提供质量监测和研究的工具.
主要方法:
- 一个级联DL模型被设计成三个子模块:图像视图分类,术后植入物检测和近端股骨骨折检测.
- 一个由10个神经网络 (ResNet,VGG,DenseNet,EfficientNet) 组成的整体模型在18834张常规部X射线图上接受了训练.
- 使用数据增强和缩放技术来提高模型性能.
主要成果:
- DL子模块实现了高精度,从92%到100%不等.
- 通过DL模型自动标记骨折,每张图像只需要0.03秒,比手动注释 (12秒/图像) 快得多.
- 用基于梯度的方法生成模型预测的视觉解释.
结论:
- 开发的半监督DL方法准确地标记关节骨折,解决手动注释的局限性.
- 这种自动化方法可以减轻与大型数据集相关的时间负担和报告不足的问题.
- 在骨骨折治疗中,DL方法为质量改善,研究和临床决策支持提供了潜在的好处.
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