Determination of &lt;i&gt;In-vivo&lt;/i&gt; Glenohumeral Translation during Loaded and Unloaded Arm Elevation

Nishinaka, Naoya; Matsuhisa, Takayuki; Takahashi, Yūji; Tsutsui, Hiroaki; Sambe, Takeyuki

doi:10.15369/sujms.28.309

Cited by 3 publications

(4 citation statements)

References 24 publications

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“…We observed a load-induced increase in scapular rotation in healthy shoulders and in shoulders with rotator cuff tears. Glenohumeral translation changed with additional weight neither in healthy shoulders, as reported also by Nishinaka et al [ 30 ], nor in shoulders with rotator cuff tears during 30° abduction. Therefore, in this cohort, shoulders with rotator cuff tears and healthy shoulders appear to behave similarly under load, with upward scapular rotation increasing during 30° abduction so as not to alter glenohumeral joint stability.…”

Section: Discussionmentioning

confidence: 56%

“…Some of the inconsistencies in literature regarding kinematics may arise from the different acquisition techniques, such as conventional radiographs [ 9 , 23 , 25 , 26 ] or single/dual-plane fluoroscopy combined with three-dimensional (3D)-to-two-dimensional (2D) model-to-image registration techniques [ 20 , 22 , 24 , 27 – 30 ]. In addition, shoulder kinematics may differ under quasistatic and dynamic conditions [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

“…However, the effect of load on shoulder kinematics has been poorly investigated. Only a few studies have examined such an effect on glenohumeral translation, and these have been in healthy shoulders, with inconsistent results [ 21 , 23 , 25 , 30 ]. Therefore, the effect of loading on shoulder kinematics in shoulders with rotator cuff disorders remains to be investigated.…”

Section: Introductionmentioning

confidence: 99%

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Severity of rotator cuff disorders and additional load affect fluoroscopy-based shoulder kinematics during arm abduction

Croci,

Hess,

Genter

et al. 2024

J Orthop Traumatol

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Background Rotator cuff disorders, whether symptomatic or asymptomatic, may result in abnormal shoulder kinematics (scapular rotation and glenohumeral translation). This study aimed to investigate the effect of rotator cuff tears on in vivo shoulder kinematics during a 30° loaded abduction test using single-plane fluoroscopy. Materials and methods In total, 25 younger controls, 25 older controls and 25 patients with unilateral symptomatic rotator cuff tears participated in this study. Both shoulders of each participant were analysed and grouped on the basis of magnetic resonance imaging into healthy, rotator cuff tendinopathy, asymptomatic and symptomatic rotator cuff tears. All participants performed a bilateral 30° arm abduction and adduction movement in the scapular plane with handheld weights (0, 2 and 4 kg) during fluoroscopy acquisition. The range of upward–downward scapular rotation and superior–inferior glenohumeral translation were measured and analysed during abduction and adduction using a linear mixed model (loads, shoulder types) with random effects (shoulder ID). Results Scapular rotation was greater in shoulders with rotator cuff tendinopathy and asymptomatic rotator cuff tears than in healthy shoulders. Additional load increased upward during abduction and downward during adduction scapular rotation (P < 0.001 in all groups but rotator cuff tendinopathy). In healthy shoulders, upward scapular rotation during 30° abduction increased from 2.3° with 0-kg load to 4.1° with 4-kg load and on shoulders with symptomatic rotator cuff tears from 3.6° with 0-kg load to 6.5° with 4-kg load. Glenohumeral translation was influenced by the handheld weights only in shoulders with rotator cuff tendinopathy (P ≤ 0.020). Overall, superior glenohumeral translation during 30° abduction was approximately 1.0 mm with all loads. Conclusions The results of glenohumeral translation comparable to control but greater scapular rotations during 30° abduction in the scapular plane in rotator cuff tears indicate that the scapula compensates for rotator cuff deficiency by rotating. Further analysis of load-dependent joint stability is needed to better understand glenohumeral and scapula motion. Level of evidence: Level 2. Trial registration: Ethical approval was obtained from the regional ethics committee (Ethics Committee Northwest Switzerland EKNZ 2021-00182), and the study was registered at clinicaltrials.gov on 29 March 2021 (trial registration number NCT04819724, https://clinicaltrials.gov/ct2/show/NCT04819724).

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Section: Discussionmentioning

confidence: 56%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Severity of rotator cuff disorders and additional load affect fluoroscopy-based shoulder kinematics during arm abduction

Croci,

Hess,

Genter

et al. 2024

J Orthop Traumatol

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“…In contrast, Teyhen et al [ 9 ] found a more superior humeral head position during abduction, and similar results were reported by Chopp et al [ 7 ] up to 90° abduction. Only Nishinaka et al [ 8 ] compared shoulder kinematics of loaded (3 kg) and unloaded arm abduction, but did not assess the differences between conditions, and reported an average of 2 mm translation from 0° to 90° abduction.…”

Section: Introductionmentioning

confidence: 99%

Reliability of the Fluoroscopic Assessment of Load-Induced Glenohumeral Translation during a 30° Shoulder Abduction Test

et al. 2022

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Rotator cuff tears are often linked to superior translational instability, but a thorough understanding of glenohumeral motion is lacking. This study aimed to assess the reliability of fluoroscopically measured glenohumeral translation during a shoulder abduction test. Ten patients with rotator cuff tears participated in this study. Fluoroscopic images were acquired during 30° abduction and adduction in the scapular plane with and without handheld weights of 2 kg and 4 kg. Images were labelled by two raters, and inferior–superior glenohumeral translation was calculated. During abduction, glenohumeral translation (mean (standard deviation)) ranged from 3.3 (2.2) mm for 0 kg to 4.1 (1.8) mm for 4 kg, and from 2.3 (1.5) mm for 0 kg to 3.8 (2.2) mm for 4 kg for the asymptomatic and symptomatic sides, respectively. For the translation range, moderate to good interrater (intra-class correlation coefficient ICC [95% confidence interval (CI)]; abduction: 0.803 [0.691; 0.877]; adduction: 0.705 [0.551; 0.813]) and intrarater reliabilities (ICC [95% CI]; abduction: 0.817 [0.712; 0.887]; adduction: 0.688 [0.529; 0.801]) were found. Differences in the translation range between the repeated measurements were not statistically significant (mean difference, interrater: abduction, −0.1 mm, p = 0.686; adduction, −0.1 mm, p = 0.466; intrarater: abduction 0.0 mm, p = 0.888; adduction, 0.2 mm, p = 0.275). This method is suitable for measuring inferior–superior glenohumeral translation in the scapular plane.

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Fully automatic algorithm for detecting and tracking anatomical shoulder landmarks on fluoroscopy images with artificial intelligence

Croci,

Hess,

Warmuth

et al. 2023

Eur Radiol

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Objective Patients with rotator cuff tears present often with glenohumeral joint instability. Assessing anatomic angles and shoulder kinematics from fluoroscopy requires labelling of specific landmarks in each image. This study aimed to develop an artificial intelligence model for automatic landmark detection from fluoroscopic images for motion tracking of the scapula and humeral head. Materials and methods Fluoroscopic images were acquired for both shoulders of 25 participants (N = 12 patients with unilateral rotator cuff tear, 6 men, mean (standard deviation) age: 63.7 ± 9.7 years; 13 asymptomatic subjects, 7 men, 58.2 ± 8.9 years) during a 30° arm abduction and adduction movement in the scapular plane with and without handheld weights of 2 and 4 kg. A 3D full-resolution convolutional neural network (nnU-Net) was trained to automatically locate five landmarks (glenohumeral joint centre, humeral shaft, inferior and superior edges of the glenoid and most lateral point of the acromion) and a calibration sphere. Results The nnU-Net was trained with ground-truth data from 6021 fluoroscopic images of 40 shoulders and tested with 1925 fluoroscopic images of 10 shoulders. The automatic landmark detection algorithm achieved an accuracy above inter-rater variability and slightly below intra-rater variability. All landmarks and the calibration sphere were located within 1.5 mm, except the humeral landmark within 9.6 mm, but differences in abduction angles were within 1°. Conclusion The proposed algorithm detects the desired landmarks on fluoroscopic images with sufficient accuracy and can therefore be applied to automatically assess shoulder motion, scapular rotation or glenohumeral translation in the scapular plane. Clinical relevance statement This nnU-net algorithm facilitates efficient and objective identification and tracking of anatomical landmarks on fluoroscopic images necessary for measuring clinically relevant anatomical configuration (e.g. critical shoulder angle) and enables investigation of dynamic glenohumeral joint stability in pathological shoulders. Key Points • Anatomical configuration and glenohumeral joint stability are often a concern after rotator cuff tears. • Artificial intelligence applied to fluoroscopic images helps to identify and track anatomical landmarks during dynamic movements. • The developed automatic landmark detection algorithm optimised the labelling procedures and is suitable for clinical application.

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Determination of <i>In-vivo</i> Glenohumeral Translation during Loaded and Unloaded Arm Elevation

Cited by 3 publications

References 24 publications

Severity of rotator cuff disorders and additional load affect fluoroscopy-based shoulder kinematics during arm abduction

Severity of rotator cuff disorders and additional load affect fluoroscopy-based shoulder kinematics during arm abduction

Reliability of the Fluoroscopic Assessment of Load-Induced Glenohumeral Translation during a 30° Shoulder Abduction Test

Fully automatic algorithm for detecting and tracking anatomical shoulder landmarks on fluoroscopy images with artificial intelligence

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