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用于婴儿髋关节超声扫描的全自动Graf标准平面和角度评估方法的开发。

Development of a Fully Automated Graf Standard Plane and Angle Evaluation Method for Infant Hip Ultrasound Scans.

作者信息

Chen Tao, Zhang Yuxiao, Wang Bo, Wang Jian, Cui Ligang, He Jingnan, Cong Longfei

机构信息

Department of Ultrasound, Beijing Jishuitan Hospital, The 4th Clinical College, Peking University, Beijing 100035, China.

Shenzhen Mindray Bio-Medical Electronics Co., Ltd., Shenzhen 518057, China.

出版信息

Diagnostics (Basel). 2022 Jun 9;12(6):1423. doi: 10.3390/diagnostics12061423.

Abstract

BACKGROUND

Graf's method is currently the most commonly used ultrasound-based technique for the diagnosis of developmental dysplasia of the hip (DDH). However, the efficiency and accuracy of diagnosis are highly affected by the sonographers' qualification and the time and effort expended, which has a significant intra- and inter-observer variability.

METHODS

Aiming to minimize the manual intervention in the diagnosis process, we developed a deep learning-based computer-aided framework for the DDH diagnosis, which can perform fully automated standard plane detection and angle measurement for Graf type I and type II hips. The proposed framework is composed of three modules: an anatomical structure detection module, a standard plane scoring module, and an angle measurement module. This framework can be applied to two common clinical scenarios. The first is the static mode, measurement and classification are performed directly based on the given standard plane. The second is the dynamic mode, where a standard plane from ultrasound video is first determined, and measurement and classification are then completed. To the best of our knowledge, our proposed framework is the first CAD method that can automatically perform the entire measurement process of Graf's method.

RESULTS

In our experiments, 1051 US images and 289 US videos of Graf type I and type II hips were used to evaluate the performance of the proposed framework. In static mode, the mean absolute error of α, β angles are 1.71° and 2.40°, and the classification accuracy is 94.71%. In dynamic mode, the mean absolute error of α, β angles are 1.97° and 2.53°, the classification accuracy is 89.51%, and the running speed is 31 fps.

CONCLUSIONS

Experimental results demonstrate that our fully automated framework can accurately perform standard plane detection and angle measurement of an infant's hip at a fast speed, showing great potential for clinical application.

摘要

背景

Graf法是目前诊断发育性髋关节发育不良(DDH)最常用的超声技术。然而,诊断的效率和准确性受超声检查人员资质以及所花费的时间和精力影响很大,存在显著的观察者内和观察者间差异。

方法

为尽量减少诊断过程中的人工干预,我们开发了一种基于深度学习的计算机辅助框架用于DDH诊断,该框架可对Graf I型和II型髋关节进行全自动标准平面检测和角度测量。所提出的框架由三个模块组成:解剖结构检测模块、标准平面评分模块和角度测量模块。该框架可应用于两种常见临床场景。第一种是静态模式,直接基于给定的标准平面进行测量和分类。第二种是动态模式,首先从超声视频中确定一个标准平面,然后完成测量和分类。据我们所知,我们提出的框架是第一种能够自动执行Graf法整个测量过程的计算机辅助诊断(CAD)方法。

结果

在我们的实验中,使用了1051张Graf I型和II型髋关节的超声图像以及289段超声视频来评估所提出框架的性能。在静态模式下,α、β角的平均绝对误差分别为1.71°和2.40°,分类准确率为94.71%。在动态模式下,α、β角的平均绝对误差分别为1.97°和2.53°,分类准确率为89.51%,运行速度为31帧/秒。

结论

实验结果表明,我们的全自动框架能够快速、准确地对婴儿髋关节进行标准平面检测和角度测量,具有很大的临床应用潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1369/9222165/51eee68a0792/diagnostics-12-01423-g001.jpg

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