https://scholars.lib.ntu.edu.tw/handle/123456789/633187
標題: | Deep learning algorithm for predicting subacromial motion trajectory: Dynamic shoulder ultrasound analysis | 作者: | YI-CHUNG SHU Lo, Yu Cheng Chiu, Hsiao Chi Chen, Lan Rong CHE-YU LIN WEI-TING WU Özçakar, Levent KE-VIN CHANG |
關鍵字: | Convolution neural network | Deep learning | Self-transfer learning | Sonography | Subacromial impingement | 公開日期: | 1-九月-2023 | 卷: | 134 | 來源出版物: | Ultrasonics | 摘要: | 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. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/633187 | ISSN: | 0041624X | DOI: | 10.1016/j.ultras.2023.107057 |
顯示於: | 應用力學研究所 |
在 IR 系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。