Institute of Applied Mechanics, College of Engineering, National Taiwan University, Taipei, Taiwan.
Department of Physical Medicine and Rehabilitation and Community and Geriatric Research Center, National Taiwan University Hospital, Bei-Hu Branch, Taipei, Taiwan.
Ultrasonics. 2023 Sep;134:107057. doi: 10.1016/j.ultras.2023.107057. Epub 2023 May 30.
Subacromial motion metrics can be extracted from dynamic shoulder ultrasonography, which is useful for identifying abnormal motion patterns in painful shoulders. However, frame-by-frame manual labeling of anatomical landmarks in ultrasound images is time consuming. The present study aims to investigate the feasibility of a deep learning algorithm for extracting subacromial motion metrics from dynamic ultrasonography. Dynamic ultrasound imaging was retrieved by asking 17 participants to perform cyclic shoulder abduction and adduction along the scapular plane, whereby the trajectory of the humeral greater tubercle (in relation to the lateral acromion) was depicted by the deep learning algorithm. Extraction of the subacromial motion metrics was conducted using a convolutional neural network (CNN) or a self-transfer learning-based (STL)-CNN with or without an autoencoder (AE). The mean absolute error (MAE) compared with the manually-labeled data (ground truth) served as the main outcome variable. Using eight-fold cross-validation, the average MAE was proven to be significantly higher in the group using CNN than in those using STL-CNN or STL-CNN+AE for the relative difference between the greater tubercle and lateral acromion on the horizontal axis. The MAE for the localization of the two aforementioned landmarks on the vertical axis also seemed to be enlarged in those using CNN compared with those using STL-CNN. In the testing dataset, the errors in relation to the ground truth for the minimal vertical acromiohumeral distance were 0.081-0.333 cm using CNN, compared with 0.002-0.007 cm using STL-CNN. We successfully demonstrated the feasibility of a deep learning algorithm for automatic detection of the greater tubercle and lateral acromion during dynamic shoulder ultrasonography. Our framework also demonstrated the capability of capturing the minimal vertical acromiohumeral distance, which is the most important indicator of subacromial motion metrics in daily clinical practice.
从动态肩部超声中可以提取肩峰下运动学指标，这对于识别肩部疼痛的异常运动模式很有用。然而，对超声图像中的解剖学标志进行逐帧手动标记是很耗时的。本研究旨在探讨一种深度学习算法从动态超声中提取肩峰下运动学指标的可行性。通过让 17 名参与者在肩胛平面上进行周期性的肩部外展和内收运动，获取动态超声图像。深度学习算法描绘了肱骨大结节（相对于外侧肩峰）的轨迹。使用卷积神经网络（CNN）或基于自我迁移学习的（STL）-CNN 提取肩峰下运动学指标，并带有或不带有自动编码器（AE）。与手动标记数据（ground truth）相比，平均绝对误差（MAE）作为主要的结果变量。使用 8 折交叉验证，与使用 STL-CNN 或 STL-CNN+AE 的组相比，使用 CNN 的组在大结节和外侧肩峰之间的水平轴上的相对差异的 MAE 被证明显著更高。在垂直轴上定位上述两个标志的 MAE 似乎也在使用 CNN 的组中比在使用 STL-CNN 的组中更大。在测试数据集上，与使用 STL-CNN 的组相比，使用 CNN 的组与真实值的误差最小垂直肩峰肱骨头距离为 0.081-0.333cm。我们成功地证明了一种深度学习算法用于自动检测动态肩部超声中大结节和外侧肩峰的可行性。我们的框架还展示了捕捉最小垂直肩峰肱骨头距离的能力，这是日常临床实践中肩峰下运动学指标的最重要指标。
