Deep learning algorithm for predicting subacromial motion trajectory: Dynamic shoulder ultrasound analysis

Shu Y., Lo Y., Chiu H., Chen L., Lin C., Wu W., ...Daha Fazla

Ultrasonics, cilt.134, 2023 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 134
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1016/j.ultras.2023.107057
  • Dergi Adı: Ultrasonics
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Compendex, Computer & Applied Sciences, EMBASE, INSPEC, MEDLINE, MLA - Modern Language Association Database
  • Anahtar Kelimeler: Convolution neural network, Deep learning, Self-transfer learning, Sonography, Subacromial impingement
  • Hacettepe Üniversitesi Adresli: Evet

Özet

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.