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

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 ).

Effect of patient-specific scapular morphology on the glenohumeral joint force and shoulder muscle force equilibrium: a study of rotator cuff tear and osteoarthritis patients.

Introduction: Osteoarthritis (OA) and rotator cuff tear (RCT) pathologies have distinct scapular morphologies that impact disease progression. Previous studies examined the correlation between scapular morphology and glenohumeral joint biomechanics through critical shoulder angle (CSA) variations. In abduction, higher CSAs, common in RCT patients, increase vertical shear force and rotator cuff activation, while lower CSAs, common in OA patients, are associated with higher compressive force. However, the impact of the complete patient-specific scapular morphology remains unexplored due to challenges in establishing personalized models. Methods: CT data of 48 OA patients and 55 RCT patients were collected. An automated pipeline customized the AnyBody™ model with patient-specific scapular morphology and glenohumeral joint geometry. Biomechanical simulations calculated glenohumeral joint forces and instability ratios (shear-to-compressive forces). Moment arms and torques of rotator cuff and deltoid muscles were analyzed for each patient-specific geometry. Results and discussion: This study confirms the increased instability ratio on the glenohumeral joint in RCT patients during abduction (mean maximum is 32.80% higher than that in OA), while OA patients exhibit a higher vertical instability ratio in flexion (mean maximum is 24.53% higher than that in RCT) due to the increased inferior vertical shear force. This study further shows lower total joint force in OA patients than that in RCT patients (mean maximum total force for the RCT group is 11.86% greater than that for the OA group), attributed to mechanically advantageous muscle moment arms. The findings highlight the significant impact of the glenohumeral joint center positioning on muscle moment arms and the total force generated. We propose that the RCT pathomechanism is related to force magnitude, while the OA pathomechanism is associated with the shear-to-compressive loading ratio. Overall, this research contributes to the understanding of the impact of the complete 3D scapular morphology of the individual on shoulder biomechanics.

Lower Trapezius and Latissimus Dorsi Transfer relieve Teres Minor Activity into the Physiological Range in Collin D Irreparable Posterosuperior Massive Rotator Cuff Tears: A Biomechanical Analysis.

BACKGROUND: Tendon transfers are established techniques to regain external rotation mobility in patients suffering from an irreparable, posterosuperior massive rotator cuff tear (MRCT). Posterosuperior MRCT with intact teres minor (Type D MRCT) can lead to excessive teres minor loading to maintain external rotation. We hypothesize that tendon transfers are effective in relieving teres minor loading in Type D MRCTs. Our aim was to biomechanically assess muscle synergism with latissimus dorsi (LD-Transfer) and lower trapezius (LT-Transfer) tendon transfer during external rotation at different abduction heights. METHODS: Using musculoskeletal modeling, we analyzed and compared the moment arm, muscle torque and muscle activity between a healthy and Type D MRCT pathological model with and without the LD- or LT-Transfer at infraspinatus and teres minor insertion sites. Output measures were analyzed during external rotation at different abduction angles and 10 to 50N resistance against external rotation. We assessed its impact on teres minor loading in a Type D MRCT. Morphological variations were parameterized using the critical shoulder angle and the acromiohumeral distance to address variations among patients. RESULTS: Both transfer types reduced teres minor torque and activity significantly, reaching physiological state at 40N external resistance (p<0.001), with insertion to infraspinatus site being more effective than teres minor site (p<0.001). External rotation moment arms of LD-Transfer were larger than LT-Transfer at 90° abduction (25.1±0.8mm vs. 21.2±0.6mm, p<0.001) and vice versa at 0° abduction (17.4±0.5mm vs. 24.0±0.2mm, p<0.001). While the healthy infraspinatus was the main external rotator in all abduction angles (50-70% torque), a Type D MRCT resulted in a 70-90% increase of teres minor torque and an up to sevenfold increase in its activity leading to excessive loadings beyond 10N resistance against external rotation. Varying the critical shoulder angle and the acromiohumeral distance led to minor variations in muscle moment arm and muscle activity. CONCLUSION: We identified biomechanical efficacy of both tendon transfers in Type D MRCT regarding teres minor load relieve and superior performance of the transfers at the infraspinatus insertion site.

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

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.

The effect of pathological shoulder rhythm on muscle and joint forces after reverse shoulder arthroplasty, a numerical analysis.

BACKGROUND: Compromised abduction ability after reverse shoulder arthroplasty is primarily linked to limited glenohumeral range of motion while scapulothoracic mobility can typically be maintained. Glenohumeral joint forces strongly depend on the resulting scapulohumeral rhythm, however, an association between the acting muscle and joint forces and the subject-specific scapulohumeral rhythm after reverse shoulder arthroplasty has not been established. METHODS: Eleven reverse shoulder arthroplasty patients were divided into groups of poor and excellent abduction ability. Subject-specific models were developed and scaled for each patient using existing motion capture data in AnyBody™. Shoulder muscle and joint forces were obtained using inverse dynamics calculations during shoulder abduction to 100° in the scapula plane. The scapulohumeral rhythm, the resting abduction angle and internal body forces between the outcome groups were compared using a Mann Whitney U test. FINDINGS: The mean glenohumeral and scapulothoracic contribution to overall shoulder abduction for the excellent group was on average 9.7% higher and 21.4% lower, respectively, compared to the mean of the poor group. For shoulder abduction angles between 30° and 60°, the excellent group demonstrated on average 25% higher muscle forces in the anterior deltoid which was significantly higher compared to the poor outcome patients. Scapulothoracic muscle activity did not differ significantly between the two functional groups. INTERPRETATION: Accordingly, rehabilitation strategies focusing on strengthening the anterior part of the deltoid in particular may improve clinical outcomes.

Deep-Learning-Based Segmentation of the Shoulder from MRI with Inference Accuracy Prediction.

Three-dimensional (3D)-image-based anatomical analysis of rotator cuff tear patients has been proposed as a way to improve repair prognosis analysis to reduce the incidence of postoperative retear. However, for application in clinics, an efficient and robust method for the segmentation of anatomy from MRI is required. We present the use of a deep learning network for automatic segmentation of the humerus, scapula, and rotator cuff muscles with integrated automatic result verification. Trained on N = 111 and tested on N = 60 diagnostic T1-weighted MRI of 76 rotator cuff tear patients acquired from 19 centers, a nnU-Net segmented the anatomy with an average Dice coefficient of 0.91 ± 0.06. For the automatic identification of inaccurate segmentations during the inference procedure, the nnU-Net framework was adapted to allow for the estimation of label-specific network uncertainty directly from its subnetworks. The average Dice coefficient of segmentation results from the subnetworks identified labels requiring segmentation correction with an average sensitivity of 1.0 and a specificity of 0.94. The presented automatic methods facilitate the use of 3D diagnosis in clinical routine by eliminating the need for time-consuming manual segmentation and slice-by-slice segmentation verification.

Automated quantification of cartilage quality for hip treatment decision support.

PURPOSE: Preservation surgery can halt the progress of joint degradation, preserving the life of the hip; however, outcome depends on the existing cartilage quality. Biochemical analysis of the hip cartilage utilizing MRI sequences such as delayed gadolinium-enhanced MRI of cartilage (dGEMRIC), in addition to morphological analysis, can be used to detect early signs of cartilage degradation. However, a complete, accurate 3D analysis of the cartilage regions and layers is currently not possible due to a lack of diagnostic tools. METHODS: A system for the efficient automatic parametrization of the 3D hip cartilage was developed. 2D U-nets were trained on manually annotated dual-flip angle (DFA) dGEMRIC for femoral head localization and cartilage segmentation. A fully automated cartilage sectioning pipeline for analysis of central and peripheral regions, femoral-acetabular layers, and a variable number of section slices, was developed along with functionality for the automatic calculation of dGEMRIC index, thickness, surface area, and volume. RESULTS: The trained networks locate the femoral head and segment the cartilage with a Dice similarity coefficient of 88 ± 3 and 83 ± 4% on DFA and magnetization-prepared 2 rapid gradient-echo (MP2RAGE) dGEMRIC, respectively. A completely automatic cartilage analysis was performed in 18s, and no significant difference for average dGEMRIC index, volume, surface area, and thickness calculated on manual and automatic segmentation was observed. CONCLUSION: An application for the 3D analysis of hip cartilage was developed for the automated detection of subtle morphological and biochemical signs of cartilage degradation in prognostic studies and clinical diagnosis. The segmentation network achieved a 4-time increase in processing speed without loss of segmentation accuracy on both normal and deformed anatomy, enabling accurate parametrization. Retraining of the networks with the promising MP2RAGE protocol would enable analysis without the need for B1 inhomogeneity correction in the future.

MRI-based 3D models of the hip joint enables radiation-free computer-assisted planning of periacetabular osteotomy for treatment of hip dysplasia using deep learning for automatic segmentation.

INTRODUCTION: Both Hip Dysplasia(DDH) and Femoro-acetabular-Impingement(FAI) are complex three-dimensional hip pathologies causing hip pain and osteoarthritis in young patients. 3D-MRI-based models were used for radiation-free computer-assisted surgical planning. Automatic segmentation of MRI-based 3D-models are preferred because manual segmentation is time-consuming.To investigate(1) the difference and(2) the correlation for femoral head coverage(FHC) between automatic MR-based and manual CT-based 3D-models and (3) feasibility of preoperative planning in symptomatic patients with hip diseases. METHODS: We performed an IRB-approved comparative, retrospective study of 31 hips(26 symptomatic patients with hip dysplasia or FAI). 3D MRI sequences and CT scans of the hip were acquired. Preoperative MRI included axial-oblique T1 VIBE sequence(0.8 mm3 isovoxel) of the hip joint. Manual segmentation of MRI and CT scans were performed. Automatic segmentation of MRI-based 3D-models was performed using deep learning. RESULTS: (1)The difference between automatic and manual segmentation of MRI-based 3D hip joint models was below 1 mm(proximal femur 0.2 ±â€¯0.1 mm and acetabulum 0.3 ±â€¯0.5 mm). Dice coefficients of the proximal femur and the acetabulum were 98 % and 97 %, respectively. (2)The correlation for total FHC was excellent and significant(r = 0.975, p < 0.001) between automatic MRI-based and manual CT-based 3D-models. Correlation for total FHC (r = 0.979, p < 0.001) between automatic and manual MR-based 3D models was excellent.(3)Preoperative planning and simulation of periacetabular osteotomy was feasible in all patients(100 %) with hip dysplasia or acetabular retroversion. CONCLUSIONS: Automatic segmentation of MRI-based 3D-models using deep learning is as accurate as CT-based 3D-models for patients with hip diseases of childbearing age. This allows radiation-free and patient-specific preoperative simulation and surgical planning of periacetabular osteotomy for patients with DDH.

Noninvasive Registration Strategies and Advanced Image Guidance Technology for Submillimeter Surgical Navigation Accuracy in the Lateral Skull Base.

HYPOTHESIS: Combining novel registration strategies and advanced image guidance technology enable submillimeter accurate and noninvasive navigation for middle ear and lateral skull base surgery. BACKGROUND: Surgery in the internal auditory canal and the petrous apex present a cognitive and motoric challenge for the surgeon. To date, image guidance rarely assists these procedures, mainly due to the lack of navigation solutions providing submillimeter accuracy associated with low cost in terms of invasiveness, radiation, and time. METHODS: This study proposes an approach to clinically viable image guidance by using a combination of advanced image guidance technology and noninvasive registration strategies. Based on accuracy-optimized optical tracking hardware (accuracy: 0.05 ±â€Š0.025 mm), 14 novel registration strategies were investigated. In human cadaveric temporal bone specimens n = 36 registration attempts per strategy were conducted. Target registration errors at 10 anatomical targets were measured. RESULTS: The most accurate registration strategies were identified as paired-point-matching using eight landmarks located in the external auditory canal and middle ear and surface matching using combined surfaces of the middle ear, the external auditory canal and the mastoid cortex yielding target registration errors of 0.51 ±â€Š0.28 mm and 0.36 ±â€Š0.13 mm respectively. CONCLUSIONS: This study demonstrates submillimeter TREs achieved with noninvasive, anatomy-based registration strategies in combination with advanced image guidance technology. Clinically viable LSB and ME navigation is realized without additional invasiveness, radiation and time associated with artificial fiducials. The appropriate registration strategy can be chosen by the surgeon depending on the pathology and surgical approach.