Subacromial Motion Metrics in Painful Shoulder Impingement: A Dynamic Quantitative Ultrasonography Analysis.

OBJECTIVES: To explore the subacromial motion metrics in patients with and without subacromial impingement syndrome (SIS) and to investigate whether the abnormality was associated with rotator cuff pathologies. DESIGN: This cross-sectional observational study used dynamic quantitative ultrasonography imaging for shoulder joint assessment. SETTING: Outpatient rehabilitation clinic. PARTICIPANTS: Individuals with SIS on at least 1 shoulder (n=32) and asymptomatic controls (n=32) (N=64). INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Frame-by-frame, the humeral greater tuberosity against the lateral edge of the acromion was traced to obtain the minimal vertical acromiohumeral distance (AHD). The rotation angle and radius of the humerus were computed using the least-squares curve fitting method. RESULTS: Approximately two-thirds of the shoulders with SIS did not have any sonographically identifiable rotator cuff pathologies. There was a consistent trend of nonsignificantly increased humeral rotation angles in painful shoulders. The generalized estimating equation demonstrated that the decreased minimal vertical AHD was associated with painful subacromial impingement (ß coefficient: -0.123cm, 95% confidence interval [CI], -0.199 to -0.047). The area under the curve for the minimal vertical AHD to discriminate painful or impinged shoulders ranged from 0.624-0.676. The increased rotation angle (ß coefficient: 10.516°; 95% CI, 3.103-17.929) and decreased rotation radius (ß coefficient: -2.903cm; 95% CI, -5.693 to -0.111) were shown to be significantly related to the presence of supraspinatus tendinopathy. CONCLUSIONS: Shoulders with SIS were characterized by a decreased minimal vertical AHD during dynamic examination. Abnormal subacromial metrics can develop in patients with mild (or no) rotator cuff pathologies. More prospective cohort studies are warranted to investigate the changes in subacromial motion metrics in populations at risk for painful or impinged shoulders.

The Potential of Ultrasound Radiomics in Carpal Tunnel Syndrome Diagnosis: A Systematic Review and Meta-Analysis.

Background: Carpal tunnel syndrome (CTS) is the most common entrapment neuropathy for which ultrasound imaging has recently emerged as a valuable diagnostic tool. This meta-analysis aims to investigate the role of ultrasound radiomics in the diagnosis of CTS and compare it with other diagnostic approaches. Methods: We conducted a comprehensive search of electronic databases from inception to September 2023. The included studies were assessed for quality using the Quality Assessment Tool for Diagnostic Accuracy Studies. The primary outcome was the diagnostic performance of ultrasound radiomics compared to radiologist evaluation for diagnosing CTS. Results: Our meta-analysis included five observational studies comprising 840 participants. In the context of radiologist evaluation, the combined statistics for sensitivity, specificity, and diagnostic odds ratio were 0.78 (95% confidence interval (CI), 0.71 to 0.83), 0.72 (95% CI, 0.59 to 0.81), and 9 (95% CI, 5 to 15), respectively. In contrast, the ultrasound radiomics training mode yielded a combined sensitivity of 0.88 (95% CI, 0.85 to 0.91), a specificity of 0.88 (95% CI, 0.84 to 0.92), and a diagnostic odds ratio of 58 (95% CI, 38 to 87). Similarly, the ultrasound radiomics testing mode demonstrated an aggregated sensitivity of 0.85 (95% CI, 0.78 to 0.89), a specificity of 0.80 (95% CI, 0.73 to 0.85), and a diagnostic odds ratio of 22 (95% CI, 12 to 41). Conclusions: In contrast to assessments by radiologists, ultrasound radiomics exhibited superior diagnostic performance in detecting CTS. Furthermore, there was minimal variability in the diagnostic accuracy between the training and testing sets of ultrasound radiomics, highlighting its potential as a robust diagnostic tool in CTS.

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

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.

Application of deep learning algorithms in automatic sonographic localization and segmentation of the median nerve: A systematic review and meta-analysis.

OBJECTIVE: High-resolution ultrasound is an emerging tool for diagnosing carpal tunnel syndrome caused by the compression of the median nerve at the wrist. This systematic review and meta-analysis aimed to explore and summarize the performance of deep learning algorithms in the automatic sonographic assessment of the median nerve at the carpal tunnel level. METHODS: PubMed, Medline, Embase, and Web of Science were searched from the earliest records to May 2022 for studies investigating the utility of deep neural networks in the evaluation of the median nerve in carpal tunnel syndrome. The quality of the included studies was evaluated using the Quality Assessment Tool for Diagnostic Accuracy Studies. The outcome variables included precision, recall, accuracy, F-score, and Dice coefficient. RESULTS: In total, seven articles were included, comprising 373 participants. The deep learning and related algorithms comprised U-Net, phase-based probabilistic active contour, MaskTrack, ConvLSTM, DeepNerve, DeepSL, ResNet, Feature Pyramid Network, DeepLab, Mask R-CNN, region proposal network, and ROI Align. The pooled values of precision and recall were 0.917 (95 % confidence interval [CI], 0.873-0.961) and 0.940 (95 % CI, 0.892-0.988), respectively. The pooled accuracy and Dice coefficient were 0.924 (95 % CI, 0.840-1.008) and 0.898 (95 % CI, 0.872-0.923), respectively, whereas the summarized F-score was 0.904 (95 % CI, 0.871-0.937). CONCLUSION: The deep learning algorithm enables automated localization and segmentation of the median nerve at the carpal tunnel level in ultrasound imaging with acceptable accuracy and precision. Future research is expected to validate the performance of deep learning algorithms in detecting and segmenting the median nerve along its entire length as well as across datasets obtained from various ultrasound manufacturers.