Motion Analysis of Triangular Fibrocartilage Complex by Using Ultrasonography Images: Preliminary Analysis

Investigation of the limiting factors of shoulder joint complex motion in college baseball players: motion analysis of the humeral head and rotator cuff using ultrasound

Background

The relationship between lower mobility, as measured by the elbow forward translation motion (T-motion) test, a new indicator of shoulder joint complex movement that measures elbow position when both dorsal hands are placed on the iliac crest while in a sitting position, and the parameters calculated by ultrasonography is unknown. The purpose of this study was to investigate the limiting factors of T-motion through motion analysis of the humeral head and rotator cuff muscles using ultrasonography in college baseball players.

Methods

Thirteen college baseball players participated in this cross-sectional study. The shortest distance from the posterior edge of the glenoid to the humeral head was measured in the static and T-motion positions, and the difference was calculated as the humeral head translation. The velocity of the infraspinatus was calculated during shoulder internal/external rotation using the particle image velocimetry method. These parameters were compared between the throwing and nonthrowing sides to examine the limiting factors of T-motion.

Results

This study indicated moderate-to-good reliability for the parameters calculated by ultrasonography. The mean anterior translation distance was significantly greater on the throwing side than on the nonthrowing side (r = 0.56, P = .015). The mean velocity of infraspinatus during internal rotation was significantly lower on the throwing side than on the nonthrowing side (r = 0.51, P = .028).

Conclusion

Increased anterior translation of the humeral head and decreased the velocity of infraspinatus are likely correlated with reduced T-motion mobility in college baseball players. These methods showed potential for physical therapy assessment and intervention to prevent shoulder dysfunction.

Dynamic Analysis of the Coracohumeral Ligament Using Ultra-Sonography in Shoulder Contracture

The coracohumeral ligament (CHL) is related to the range of motion of the shoulder joint. The evaluation of the CHL using ultrasonography (US) has been reported on the elastic modulus and thickness of the CHL, but no dynamic evaluation method has been established. We aimed to quantify the movement of the CHL by applying Particle Image Velocimetry (PIV), a technique used in the field of fluid engineering, to cases of shoulder contracture using the US. The subjects were eight patients, with 16 shoulders. The coracoid process was identified from the body surface, and a long-axis US image of the CHL parallel to the subscapularis tendon was drawn. The shoulder joint was moved from 0 degrees of internal/external rotation to 60 degrees of internal rotation at a rhythm of one reciprocation every 2 s. The velocity of the CHL movement was quantified by the PIV method. The mean magnitude velocity of CHL was significantly faster on the healthy side. The maximum magnitude velocity was significantly faster on the healthy side. The results suggest that the PIV method is helpful as a dynamic evaluation method, and in patients with shoulder contracture, the CHL velocity was significantly decreased.

Motion Analysis of the Extensor Carpi Ulnaris in Triangular Fibrocartilage Complex Injury Using Ultrasonography Images

The subsheath of the extensor carpi ulnaris (ECU) tendon, a component of the triangular fibrocartilage complex (TFCC), is particularly important as it dynamically stabilizes the distal radioulnar joint. However, the relationship between TFCC injury and ECU dynamics remains unclear. This study aimed to analyze ECU movement and morphology using ultrasonography (US) images. Twenty wrists of patients with TFCC injury, who underwent TFCC repair, were included in the injury group, and 20 wrists of healthy volunteers were in the control group. For static image analysis, curvature and linearity ratios of the ECU in US long-axis images captured during radioulnar deviation were analyzed. For dynamic analysis of the ECU, the wrist was moved from radial deviation to ulnar deviation at a constant speed, and the velocity of the tendon was analyzed using particle image velocimetry. The static analysis showed that the ECU tendon was more curved in ulnar deviation in the injury group than in the control group, and the dynamic analysis showed that only vertical velocity toward the deep side during ulnar deviation was higher in the injury group. These results suggest that TFCC injury caused ECU curvature during ulnar deviation and increased the vertical velocity of the ECU during wrist deviation.

Ultrasound With Artificial Intelligence Models Predicted Palmer 1B Triangular Fibrocartilage Complex Injuries

PURPOSE

To calculate the diagnostic accuracy from the confusion matrix using deep learning (DL) on ultrasound (US) images of Palmer 1B triangular fibrocartilage complex (TFCC) injury.

METHODS

Twenty-nine wrists of 15 healthy volunteers (11 men; mean age, 34.9 years ± 9.7) (control group) and 20 wrists of 17 patients (11 men; mean age 41.0 years ± 12.2) with TFCC injury (Palmer type IB) (injury group) were included in the study. The diagnosis of Palmer 1B TFCC injury was made using magnetic resonance imaging, computed tomography arthrography, and intraoperative arthroscopic findings. In total, 2,000 images were provided to each group, 80% of which were randomly selected by AI and used as training data; the remaining data were used as test data. Transfer learning was conducted using a pretrained 3 separate models (GoogLeNet, ResNet50, ResNet101). Model evaluation was performed using a confusion matrix. The area under a receiver operating characteristic curve was also calculated. The occlusion sensitivity was used to visualize the important features.

RESULTS

For the prediction of TFCC injury by the DL model, the best score of accuracy was 0.85 in GoogLeNet, a recall was 1.0 in ResNet50 and ResNet101, and a specificity was 0.78 in GoogLeNet. In predicting the TFCC injury for the test data, the best score of the AUC was 0.97 on ResNet101. Visualization of important features showed that AI predicted the presence of injury by focusing on the morphology of the articular disc.

CONCLUSIONS

US images using the DL model predicted Palmer 1B TFCC injury with high accuracy, with the best scores of 0.85 for accuracy on GoogLeNet, 1.00 for sensitivity on ResNet50 and ResNet101, and 0.78 for specificity on GoogLeNet. The use of DL for US imaging of Palmer 1B TFCC injury predicted the injury as well as magnetic resonance imaging and computed tomography arthrography LEVEL OF EVIDENCE: IV; retrospective case series study.