Diagnosis Of Rotator Cuff Tears Research Articles

A rare case report: Brucellosis as an extreme cause of shoulder subluxation and rotator cuff tendinopathy

Rationale: Shoulder subluxation and rotator cuff tendinopathy is commonly associated with degeneration or trauma, and brucellosis has been reported to be correlated with shoulder subacromial bursitis, arthritis. However, brucellosis as a cause of shoulder subluxation and rotator cuff tendinopathy has never been reported in the English medical literature. Patient concerns: A 46-year-old man complained of left shoulder weakness and limited movement ongoing for 1 month. He was primarily diagnosed with shoulder subluxation and rotator cuff tear based on magnetic resonance imaging, and was advised to undergo arthroscopic rotator cuff surgical repair. Diagnoses: Although rotator cuff tear was diagnosed in another hospital, some doubts remained. First, a tendon profile with a relative high signal in T2-weighted magnetic resonance images in the supraspinatus tear area still existed. The findings were clearly different from the usual full-thickness rotator cuff tear, and no tendon profile could be found in the tear area. Second, degenerative rotator cuff tear mainly occurs in the aged population and this patient was relatively young, without a history of trauma. His left shoulder was painless but the weakness and the limited movement were extreme and all of these factors did not support the diagnosis of rotator cuff tear. Some infectious disease was suspected to be the cause and relevant laboratory tests were conducted. This patient was eventually diagnosed as having brucellosis-associated shoulder subluxation and rotator cuff tendinopathy based on positive serum standard tube agglutination test and buffer plate agglutination test for brucellosis. Interventions: Oral drug therapy with rifampicin and doxycycline was used for 8 weeks. Outcomes: A complete response with normal shoulder strength and movement was achieved at 8 weeks. At the one-year follow-up no signs of recurrence were detected. Lessons: Brucellosis-associated shoulder subluxation and rotator cuff tendinopathy may be misdiagnosed as a rotator cuff tear in magnetic resonance imaging and be further managed with indeterminate arthroscopic repair. A comprehensive understanding of the patient’s previous medical history, imaging and laboratory data and a careful physical examination are of great clinic value for the accurate diagnosis of the condition and the subsequent prognosis of the patient.

Diagnostic Accuracy of USG and MRI for the Detection of Rotator Cuff Injury.

Rotator cuff disease frequently causes shoulder pain and is diagnosed using various radiological methods alongside history and physical examination. Arthrography has traditionally been employed for this purpose, but newer non-invasive techniques such as ultrasonography (USG) and magnetic resonance imaging (MRI) are increasingly used. However, no single method is universally agreed upon as the best diagnostic tool, each having its own limitations. To evaluate how effectively ultrasound and MRIcan diagnose rotator cuff tears. Seventy patients suspected of having a rotator cuff tear underwent investigations at the Radiology Department of Krishna Vishwa Vidyapeeth (Deemed to be University), Karad. USG and MRI examinations were done on the same day, along with a detailed history. USG was conducted using a GE LOGIQ P9 machine with a high-frequency 3-12 MHz transducer. MRI was conducted using a 1.5T SiemensMagnetom Avanto scanner. Pain and stiffness are the most common complaints in rotator cuff tears. The predisposing factors include male predominance, increasing age, dominant hand use, and trauma history. The supraspinatus tendon is the most frequently injured, with partial tears, especially articular surface tears, being more common than full-thickness tears. Clinical examinations, USG, and MRI are valuable in diagnosing rotator cuff tears. Our findings indicate that USGmay not be as reliable in detecting rotator cuff tears as once believed. A positive ultrasound result is more trustworthy than a negative one. In contrast, MRIdemonstrates greater sensitivity and overall diagnostic accuracy compared to both ultrasonography and clinical assessment for detecting rotator cuff tears.

Dynamic ultrasound assessment of rotator cuff tears: The finger compression technique

AbstractThe article aims to introduce the finger compression technique as a novel dynamic compression technique that can be easily incorporated into the routine sonographic examination to enhance its diagnostic performance in the investigation of rotator cuff tears. The application of the finger compression technique is demonstrated with illustrations, ultrasound images, and videos. The finger compression technique can be applied in challenging ultrasound shoulder scans. The technique enables visualisation of movement of hyperechoic debris or hypertrophic synovial tissue filling the defect of the rotator cuff tear during real‐time dynamic scanning. It also helps to milk bursal fluid into the tendon gap and across the torn tendon into the shoulder joint. The real‐time finger compression technique is a fast, easy and useful supplementary technique for confirming the diagnosis of rotator cuff tears in the presence of severe tendinopathy complicated by bursal thickening and may help to more accurately characterise rotator cuff tears when present.

Utilizing Diagnostic Musculoskeletal Ultrasound for Assessment of the Infraspinatus Muscle and Tendon: Implications for Rehabilitation Professionals

The rotator cuff, comprising the subscapularis, supraspinatus, infraspinatus, and teres minor muscles, plays a crucial role in stabilizing the glenohumeral joint by securing the head of the humerus within the glenoid cavity of the scapula. The tendinous insertions of these muscles generate tension within the capsule, enhancing joint stability during muscular activity. The rotator cuff is susceptible to damage from disease, injury, or trauma, which can result in tears or ruptures of one or more tendons. The evaluation of the infraspinatus muscle and tendon is vital for diagnosing and managing various shoulder pathologies. Accurate imaging to determine the specific muscle involvement and injury severity significantly impacts treatment decisions. Diagnostic musculoskeletal ultrasound (MSK-US) has emerged as a valuable tool for assessing the infraspinatus muscle and tendon, offering real-time, dynamic assessment capabilities essential for precise diagnosis and effective rehabilitation planning. This article reviews the utility and advantages of MSK-US in evaluating the infraspinatus muscle and tendon, emphasizing technique specifics, diagnostic accuracy, and comparative efficacy against other imaging modalities. It details a systematic approach to the ultrasound examination technique for the infraspinatus, including patient positioning and identification of common pathologies such as tears, tendinopathy, and calcifications. With recent advancements in transducer strength, image resolution, and operator training, ultrasound serves as an excellent alternative imaging modality for diagnosing rotator cuff tears. This article aims to equip rehabilitation professionals with a comprehensive understanding of MSK-US as a diagnostic tool for the infraspinatus, promoting more precise diagnosis, treatment planning and improved patient outcomes.

Machine-learning models for diagnosis of rotator cuff tears in osteoporosis patients based on anteroposterior X-rays of the shoulder joint

ObjectiveThis study aims to diagnose Rotator Cuff Tears (RCT) and classify the severity of RCT in patients with Osteoporosis (OP) through the analysis of shoulder joint anteroposterior (AP) X-ray-based localized proximal humeral bone mineral density (BMD) measurements and clinical information based on machine learning (ML) models. MethodsA retrospective cohort of 89 patients was analyzed, including 63 with both OP and RCT (OPRCT) and 26 with OP only. The study analyzed a series of shoulder radiographs from April 2021 to April 2023. Grayscale values were measured after plotting ROIs based on AP X-rays of shoulder joint. Five kinds of ML models were developed and compared based on their performance in predicting the occurrence and severity of RCT from ROIs' greyscale values and clinical information (age, gender, advantage side, lumbar BMD, and acromion morphology (AM)). Further analysis using SHAP values illustrated the significant impact of selected features on model predictions. ResultsR1-6 had a positive correlation with BMD respectively. The nine variables, including greyscale R1-6, age, BMD, and AM, were used in the prediction models. The RF model was determined to be superior in effectively diagnosing RCT in OP patients, with high AUC scores of 0.998, 0.889, and 0.95 in the training, validation, and testing sets, respectively. SHAP values revealed that the most influential factors on the diagnostic outcomes were the grayscale values of all cancellous bones in ROIs. A column-line graph prediction model based on nine variables was constructed, and DCA curves indicated that RCT prediction in OP patients was favored based on this model. Furthermore, the RF model was also the most superior in predicting the types of RCT within the OPRCT group, with an accuracy of 86.364% and 73.684% in the training and test sets, respectively. SHAP values indicated that the most significant factor affecting the predictive outcomes was the AM, followed by the grayscale values of the greater tubercle, among others. ConclusionsML models, particularly the RF algorithm, show significant promise in diagnosing RCT occurrence and severity in OP patients using conventional shoulder X-rays based on the nine variables. This method presents a cost-effective, accessible, and non-invasive diagnostic strategy that has the potential to substantially enhance the early detection and management of RCT in OP patient population.

Automated detection and classification of the rotator cuff tear on plain shoulder radiograph using deep learning

BackgroundThe diagnosis of rotator cuff tears using radiographs alone is clinically challenging; thus, the utility of deep learning algorithms based on convolutional neural networks has been remarkable in the field of medical imaging recognition. We aimed to evaluate the diagnostic performance of artificial intelligence (a deep learning algorithm; a convolutional neural network) to detect and classify rotator cuff tears using shoulder radiographs, and compare its diagnostic performance with that of orthopedic surgeons. MethodsA total of 1,169 plain shoulder anteroposterior radiographs (one image per shoulder) were included in the total dataset and divided into four groups: intact, small, medium, and large to massive tear groups. The sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and area under the receiver operating curve were measured for the detection of rotator cuff tears through binary classification. The average accuracy, recall, precision, and F1-score were divided into four groups by cuff tear size for multiclass classification. ResultsThe convolutional neural network demonstrated a high performance, with 92% sensitivity, 69% specificity, 86% accuracy, and an AUC of 0.88 for the detection of rotator cuff tears. The average accuracy, recall, precision, and F1-score of the convolutional neural network for classification were 60%, 0.42, 0.49, and 0.45, respectively. The accuracy of the convolutional neural network for the detection and classification of rotator cuff tears was significantly better than that of orthopedic surgeons. ConclusionThe convolutional neural network demonstrated the diagnostic ability to detect and classify rotator cuff tears using plain shoulder radiographs, and the diagnostic performance exhibited equal to superior accuracy when compared with those of shoulder experts.

Investigating the Diagnostic Value of MRI in the Diagnosis of Rotator Cuff Tears Compared with Findings from Arthroscopy or Open Surgery

Abstract Introduction: Rotator cuff tear is amongst the most common shoulder diseases and a principal reason for orthopaedic consultations. Currently, magnetic resonance imaging (MRI) is one of the most common diagnostic methods for rotator cuff tears and has been compared with direct observation methods, such as arthroscopy and open surgery, in this study. Materials and Methods: The present study is a cross-sectional analytical type conducted retrospectively from the beginning of 2018 to the end of 2020 in Al-Zahra and Kashani Hospitals in Isfahan, Iraq, covering 86 patients including 54 men and 32 women with criteria for rotator cuff tear who underwent MRI and subsequently arthroscopy or open surgery. MRI images were interpreted by a radiology specialist, and physical examinations, arthroscopy and surgery were performed by two orthopaedic specialists, calculating sensitivity, specificity, accuracy, positive predictive value and negative predictive value for rotator cuff tears. Results: A total of 29 cases of partial tear and 37 cases of complete tear were observed, with sensitivity, specificity, positive predictive value, negative predictive value and accuracy obtained as 93.9%, 60%, 88.6, 75% and 86%, respectively. Conclusion: MRI is a highly reliable diagnostic tool for diagnosing rotator cuff tears, although correlation with clinical symptoms and physical examinations is essential for this diagnosis.

Compressed sensing magnetic resonance imaging (CS-MRI) diagnosis of rotator cuff tears

Compressed sensing magnetic resonance imaging (CS-MRI) diagnosis of rotator cuff tears

Diagnostic performance of a 3D double-echo steady-state sequence at 3 T using radial reformats for detecting and grading rotator cuff tears: a pilot diagnostic accuracy study with magnetic resonance imaging and arthroscopic correlation.

In diagnosing rotator cuff tears (RCTs), magnetic resonance imaging (MRI) is the imaging modality of choice, and its accuracy is improving constantly. To evaluate the diagnostic performance of a high-resolution 3D double-echo steady-state (DESS) sequence with radial and paracoronal 3-T MRI regarding the grading of RCTs in correlation with conventional 2D MRI and arthroscopic findings. We retrospectively compared arthroscopic findings of RCTs with preoperative MRI, including conventional 2D sequences and radial and paracoronal DESS images in 20 patients. Two observers evaluated supraspinatus (SSP), infraspinatus (ISP), and subscapularis (SSC) tendon tears using a grading system. For statistical analysis, arthroscopy was used as the reference standard. Inter-observer agreement for detecting and grading SSP, ISP, and SSC tendon tears on radial and paracoronal sliced 3D DESS MRI was excellent (intraclass-correlation [ICC] = 0.92-0.98; all P < 0.001). Regarding the detection of SSP lesions, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 93.8%, 50%, 88.2%, and 66.7% for both radial and paracoronal DESS imaging. A sensitivity of 100%, specificity of 61.1%, PPV of 22.2%, and NPV of 100% were noted for detecting ISP tendon tears using radially reformatted DESS images. Regarding detecting SSC tendon tears using radially reformatted DESS images, sensitivity, specificity, PPV, and NPV were 100%, 81.3%, 50%, and 100%, respectively. The results with standard 2D MRI were similar. The DESS technique with radially reformatted images provided excellent sensitivity and high inter-observer agreement in detecting RCTs. It showed a moderate to high correlation between MRI and arthroscopy for diagnosing SSP and SSC tendon tears.

Can the role of the acromion index in the diagnosis of rotator cuff tear be redefined with a different measurement technique?

Background: Many radiographic parameters associated with rotator cuff tears (RCT) have been described. Our aim is to measure the capacity to predict RCT by reinterpreting the Acromion index (AI) with a new radiographic measurement technique.&#x0D; Methods: The shoulder magnetic resonance imaging (MRI) report of a total of 62 patients and AI measured with the new technique in shoulder radiographs were evaluated. On shoulder radiographs, the glenohumeral length was identified as the length between the lateral humerus tuberculum majus and the anterior midpoint of the glenoid joint. The glenoacromial length was also defined from a different perspective as the length between the lateral tip of the acromion and the anterior midpoint of the glenoid joint.&#x0D; Results: There was no significant difference in glenohumeral length between patients with complete and partial RCT and those without RCT (p value=0.163). There was no significant difference in glenoacromial length between these three groups of patients (Pvalue=0.110). It was concluded that there was no significant difference between the three groups of patients in terms of AI that we redefined (Pvalue=0.095).&#x0D; Conclusion: AI of the glenohumeral and glenoacromial lengths, which were redefined with a different measurement technique on the shoulder radiography, did not yield statistically significant results in the diagnosis of shoulder RCT.

Comparative Study between High Resolution Ultrasound and MRI in Diagnosis of Rotator Cuff Tears

Comparative Study between High Resolution Ultrasound and MRI in Diagnosis of Rotator Cuff Tears

Deep learning-based screening tool for rotator cuff tears on shoulder radiography

BackgroundEarly diagnosis of rotator cuff tears is essential for appropriate and timely treatment. Although radiography is the most used technique in clinical practice, it is difficult to accurately rule out rotator cuff tears as an initial imaging diagnostic modality. Deep learning-based artificial intelligence has recently been applied in medicine, especially diagnostic imaging. This study aimed to develop a deep learning algorithm as a screening tool for rotator cuff tears based on radiography. MethodsWe used 2803 shoulder radiographs of the true anteroposterior view to develop the deep learning algorithm. Radiographs were labeled 0 and 1 as intact or low-grade partial-thickness rotator cuff tears and high-grade partial or full-thickness rotator cuff tears, respectively. The diagnosis of rotator cuff tears was determined based on arthroscopic findings. The diagnostic performance of the deep learning algorithm was assessed by calculating the area under the curve (AUC), sensitivity, negative predictive value (NPV), and negative likelihood ratio (LR−) of test datasets with a cutoff value of expected high sensitivity determination based on validation datasets. Furthermore, the diagnostic performance for each rotator cuff tear size was evaluated. ResultsThe AUC, sensitivity, NPV, and LR− with expected high sensitivity determination were 0.82, 84/92 (91.3%), 102/110 (92.7%), and 0.16, respectively. The sensitivity, NPV, and LR− for full-thickness rotator cuff tears were 69/73 (94.5%), 102/106 (96.2%), and 0.10, respectively, while the diagnostic performance for partial-thickness rotator cuff tears was low at 15/19 (78.9%), NPV of 102/106 (96.2%) and LR− of 0.39. ConclusionsOur algorithm had a high diagnostic performance for full-thickness rotator cuff tears. The deep learning algorithm based on shoulder radiography helps screen rotator cuff tears by setting an appropriate cutoff value. Level of evidenceLevel III: Diagnostic Study.

The value of percutaneous ultrasound-guided subacromial bursography in the diagnosis of rotator cuff tears.

AbstractPurpose To determine the feasibility and diagnostic value of percutaneous ultrasound-guided subacromial bursography (PUSB) in the diagnosis of rotator cuff tears.Methods Between July 2019 to October 2021, 78 patients who had suspected rotator cuff injury and who were admitted to the Second Affiliated Hospital of Xi'an Jiaotong University were selected, including 32 males and 46 females, aged 31-70 years (mean age 53.9±9.1 years), with a course of 1D-2 years. The MRI, US and PUSB images of patients were retrospectively analyzed to obtain the diagnostic and predictive indexes (sensitivity, specificity, positive predictive value，negative predictive value and accuracy) of these three methods for different rotator cuff tears types (full-thickness tears, partial-thickness tears and no tears of rotator cuff ). With the results of shoulder arthroscopy serving as the standard, PUSB results were compared with MRI and US results using the X2 -test (a=0.05, two-sided).Results In all 78 patients, the overall accuracy of MRI, US, and PUSB in diagnosing rotator cuff tears was 82.1% (64/78), 75.6% (59/78) and 96.2% (75/78), respectively (P < 0.001). Among 21 patients with full-thickness tears, the numbers of cases correctly diagnosed by MRI, US and PUSB were 19, 19 and 21, respectively. The sensitivity and specificity of MRI, US and PUSB in diagnosing full-thickness tears were 90.5%, 90.5%, 100% and 98.2%, 93.0%, 100%, respectively. The diagnostic accuracies of full-thickness rotator cuff tears were 90.5%, 90.5% and 100%, respectively, with no statistical difference (P = 0.344). Among 42 patients with partial-thickness tears, the numbers of patients whose cases were correctly diagnosed on MRI, US and PUSB were 32, 27 and 40, respectively. The sensitivity and specificity of MRI, US and PUSB in diagnosing partial-thickness tears were 76.2%, 64.3%, and 95.2% and 88.9%, 88.9%, and 97.2%, respectively. The diagnostic accuracies of partial-thickness rotator cuff tears were 76.2% (32/42), 64.3% (27/42) and 95.2% (40/42), respectively (P＜0.05). Among the 15 patients without tears, the numbers of misdiagnosed cases by MRI, US and PUSB were 2, 2, and 1, respectively, and they were all misdiagnosed as partial-thickness tears. The sensitivity and specificity of MRI, US and PUSB in the diagnosis of complete rotator cuff were 86.7%, 86.7%, and 93.3% and 85.7%, 82.5% and 96.8%, respectively, and the accuracies in diagnosing no tears were 86.7% (13/15), 86.7% (13/15) and 87.5% (14/15), respectively (P = 0.997).Conclusions It is feasible to diagnose rotator cuff tears by PUSB, which can be used as an important supplement imaging method to evaluate rotator cuff tears.

Handheld Ultrasound Does not Replace Magnetic Resonance Imaging for Diagnosis of Rotator Cuff Tears

The purpose of this study was to examine the reliability and validity of handheld ultrasound (HHUS) alone versus conventional ultrasound (US) or magnetic resonance imaging (MRI) for diagnosis of rotator cuff tears and versus MRI plus computed tomography (CT) for diagnosis of fatty infiltration. Adult patients with shoulder complaints were included in this study. HHUS of the shoulder was performed twice by an orthopedic surgeon and once by a radiologist. RCTs, tear width, retraction and FI were measured. Inter- and intrarater reliability of the HHUS was calculated using a Cohen's kappa coefficient. Criterion and concurrent validity were calculated using a Spearman's correlation coefficient. Sixty-one patients (64 shoulders) were included in this study. Intra-rater agreement of HHUS for assessment of RCTs (к= 0.914, supraspinatus) and FI (к= 0.844, supraspinatus) was moderate to strong. Interrater agreement was none to minimal for the diagnosis of RCTs (к= 0.465, supraspinatus) and FI (к= 0.346, supraspinatus). Concurrent validity of HHUS compared to MRI was fair for diagnosis of RCTs (r= 0.377, supraspinatus) and fair-to-moderate FI (r= 0.608, supraspinatus). HHUS shows a sensitivity of 81.1% and specificity of 62.5% for diagnosis of supraspinatus tears, 60% and 93.1% for subscapularis tears, 55.6% and 88.9% for infraspinatus tears. On the basis of findings in this study, we conclude that HHUS is an aid in diagnosis of RCTs and higher degrees of FI in patients who are not obese, but it does not replace MRI as the gold standard. Further clinical studies on the application of HHUS comparing HHUS devices in larger patient populations and healthy patients are required to identify its utility in clinical practice. Level III.

Diagnostic Accuracy and Agreement between Magnetic Resonance Imaging and Ultrasound Finding for the Diagnosis of Rotator Cuff Tears

Objective: To determine the diagnostic accuracy and agreement between ultrasound and magnetic resonance (MR) imaging in determining rotator cuff tears.&#x0D; Study Design: Cross-sectional study.&#x0D; Place and Duration of the Study: Armed Forces Institute of Radiology and Imaging, Pak Emirates Military Hospital,Rawalpindi Pakistan, from Jul 2018 to Jan 2019.&#x0D; Methodology: Patients of either gender with a traumatic shoulder injury duration of less than 15 days were consecutively included. Rotator cuff tear on ultrasonography was diagnosed on the presence of a hypoechoic discontinuity in the tendon and accentuation of cartilage shadow, giving a 'double cortex' view while on MRI, the hyper-intense signal area within the tendon on T2W, fat-suppressed and GRE sequences, corresponding to fluid signal seen.&#x0D; Results: Of 88 patients, the mean age was 54.022±5.19 years. Ultrasound diagnosed rotator cuff tears in 42(47.7%) patients, and MRI diagnosed rotator cuff tears in 44(50.0%) patients. Diagnostic accuracy of ultrasound taking MR imaging as the gold standard showed sensitivity as 81.82%, specificity as 77.27%, negative predicted value as 78.26%, positive predicted value as 80.95%, and overall diagnostic accuracy as 79.55%. A moderate agreement was found between ultrasound and MRI findings (p-value=0.591).&#x0D; Conclusion: The findings of the current study showed significant moderate agreement between ultrasound and MR imaging in the determination of rotator-cuff tears.

Detection of Shoulder Rotator Cuff Tears from X-Ray Image by Using Convolutional Neural Network

Shoulder rotator cuff tears are common disabilities of the shoulder joint caused by daily activities, sports injuries, and aging. Magnetic resonance imaging is widely used to diagnose rotator cuff tears, but radiography is a more rapid and popular imaging technique. In this study, we proposed a detection method using convolutional neural networks (CNN) on X-ray images of the shoulder for simpler diagnosis of rotator cuff tears. 139 subjects, classified into two classes according to the severity of the tear, were compared in terms of detection accuracy between five regions of interest on the shoulder and two combinations of CNN models, with a maximum accuracy of 79.3% accuracy.

Deep Learning Diagnosis and Classification of Rotator Cuff Tears on Shoulder MRI.

Detection of rotator cuff tears, a common cause of shoulder disability, can be time-consuming and subject to reader variability. Deep learning (DL) has the potential to increase radiologist accuracy and consistency. The aim of this study was to develop a prototype DL model for detection and classification of rotator cuff tears on shoulder magnetic resonance imaging into no tear, partial-thickness tear, or full-thickness tear. This Health Insurance Portability and Accountability Act-compliant, institutional review board-approved study included a total of 11,925 noncontrast shoulder magnetic resonance imaging scans from 2 institutions, with 11,405 for development and 520 dedicated for final testing. A DL ensemble algorithm was developed that used 4 series as input from each examination: fluid-sensitive sequences in 3 planes and a sagittal oblique T1-weighted sequence. Radiology reports served as ground truth for training with categories of no tear, partial tear, or full-thickness tear. A multireader study was conducted for the test set ground truth, which was determined by the majority vote of 3 readers per case. The ensemble comprised 4 parallel 3D ResNet50 convolutional neural network architectures trained via transfer learning and then adapted to the targeted domain. The final tear-type prediction was determined as the class with the highest probability, after averaging the class probabilities of the 4 individual models. The AUC overall for supraspinatus, infraspinatus, and subscapularis tendon tears was 0.93, 0.89, and 0.90, respectively. The model performed best for full-thickness supraspinatus, infraspinatus, and subscapularis tears with AUCs of 0.98, 0.99, and 0.95, respectively. Multisequence input demonstrated higher AUCs than single-sequence input for infraspinatus and subscapularis tendon tears, whereas coronal oblique fluid-sensitive and multisequence input showed similar AUCs for supraspinatus tendon tears. Model accuracy for tear types and overall accuracy were similar to that of the clinical readers. Deep learning diagnosis of rotator cuff tears is feasible with excellent diagnostic performance, particularly for full-thickness tears, with model accuracy similar to subspecialty-trained musculoskeletal radiologists.

Black Race, Hispanic Ethnicity, and Medicaid Insurance Are Associated With Lower Rates of Rotator Cuff Repair in New York State

To determine the use of operative rotator cuff repair for rotator cuff pathology in New York State and analyze the racial, ethnic, and income-based disparities in receiving rotator cuff repair. A retrospective review of the Statewide Planning and Research Cooperative System Database of New York State was conducted to include patients with a new diagnosis of rotator cuff tear between July 1, 2017, and June 30, 2019, with at least 6 months of follow-up. Bivariate analysis using χ2 tests and multivariable logistic regression models were used to determine racial, ethnic, and income-based disparities in the use of surgical treatment with rotator cuff repair. A total of 87,660 patients wereincluded in the study. Of these, 36,422 patients (41.5%) underwent surgical treatment with rotator cuff repair. Multivariable analysis showed that Black race (adjusted odds ratio [aOR] 0.78; 95% confidence interval [CI] 0.69-0.87; P< .001), Hispanic/Latino ethnicity (aOR 0.91; 95% CI 0.85-0.97); P= .004), and Medicaid (aOR 0.75; 95% CI 0.70-0.80; P < .001), or other government insurance (aOR 0.82; 95% CI 0.78-0.86; P < .001) were independently associated with lowerrates of rotator cuff repair. Male sex (aOR 1.18; 95% CI 1.14-1.22; P < .001), Asian race (aOR 1.27; 95% CI 1.00-1.62; P= .048), workers' compensation insurance (aOR 1.12; 95% CI 1.07-1.18; P < .001), and greater home ZIP code income quartile (aOR 1.19; 95% CI 1.09-1.30; P < .001) were independently associated with greater rates of operative management. Although race was an independent covariate affecting rate of rotator cuff repair, the effects of race were altered when accounting for the other covariates, suggesting that race alone does not account for the differences in rate of surgery for rotator cuff pathology. In this analysis of all adult patients presenting with rotator cuff tears to New York hospital systems from 2017 to 2019, we identified significant racial, ethnic, and socioeconomic disparities in the likelihood of rotator cuff repair surgery for patients with rotator cuff tears. These include lower rates of rotator cuff repair for those Black, Hispanic, and low-income populations as represented by Medicaid insurance and low home ZIP code income quartile. This study reports disparities in the use of rotator cuff repair for individuals with rotator cuff pathology.

Poster 174: The Role of Familial Predisposition in Imaging-Confirmed Atraumatic Rotator Cuff Tears

Objectives:The etiology of rotator cuff tears is not completely understood. Several studies have identified age, smoking, hypercholesterolemia, diabetes, and hypertension as potential risk factors of rotator cuff tears. Limited data suggests the role of genetic and familial predisposition in the etiology of rotator cuff tears. Our objective was to assess whether there is an increased likelihood of rotator cuff tears in family members of patients with rotator cuff tears versus those without tears. This would provide evidence for whether there is a familial predisposition to rotator cuff tears.Methods:Patients presenting to a shoulder clinic with pain, stiffness, or instability were recruited in this study. Patients that completed a shoulder outcomes questionnaire, were age 18 years or older, and had an available MRI or CT arthrogram of the symptomatic shoulder were included in the study. Any individuals with acute traumatic rotator cuff tears were excluded from analysis. On the questionnaire, patients provided information on demographics, personal medical history, shoulder symptoms, and family history of rotator cuff tears. The diagnosis of rotator cuff tears was based on imaging (MRI or CT arthrogram) confirmation of a structural defect in the rotator cuff. The association between family history of rotator cuff problems and the likelihood of an imaging-confirmed rotator cuff tear diagnosis was evaluated using multivariate logistic regression, adjusting for age, sex, race and ethnicity, smoking, hypertension, diabetes, and depression.Results:In our cohort of 2,337 patients, 52.7% (n=1,231) of patients had an imaging-confirmed atraumatic rotator cuff tear. Among patients with tears, 18.0% (n=221) of patients reported a family history of rotator cuff issues versus 11% (n=123) in patients without tears. A family history of rotator cuff problems was significantly associated with the diagnosis of an imaging-confirmed rotator cuff tear (Odds Ratio=2.27, 95% CI = 1.72, 3.01). Increasing age (Odds Ratio=1.06, 95% CI = 1.05, 1.07) and Hispanic race and ethnicity (Odds Ratio=1.49, 95% CI = 1.06, 2.11 in Hispanic patients versus those of Non-Hispanic White ancestry) were also significantly associated with a rotator cuff tear diagnosis. Sex, smoking, hypertension, diabetes, and depression were not significantly associated with rotator cuff tears.Conclusions:Our study shows that individuals with rotator cuff tears were over two times as likely to have a family member with a rotator cuff tear as compared with patients without tears. These results support previous studies which have suggested a genetic component in the development of tendinopathies1.In a similar study by Assunção et al., individuals with familial history of treatment for rotator cuff tears were 3.3 times as likely to experience a cuff tear2. In another study by Harvie et al, siblings of patients with a full-thickness tear were 2.42 times more likely to experience a tear

Poster 153: Dynamic Radiographic Evaluation of Scapulohumeral Rhythm in Patients with Massive vs Small Rotator Cuff Tears

Objectives:The management of rotator cuff tears varies based on patient age, function, tear size, and chronicity. Treatment of massive tears results in less favorable or predictable outcome, presenting a challenge in determining the optimal intervention. Classification of a tear as “massive” is often based on MRI and confirmed intraoperatively. However, the use of a functional measure of shoulder motion, such as the scapulohumeral rhythm (SHR), offers an alternate, clinically relevant method to help guide diagnosis and surgical management. Defined as the ratio of glenohumeral (GH) to scapulothoracic (ST) motion, SHR is known to be altered in patients with rotator cuff tears. While previously challenging to measure SHR in a clinical setting, a novel technique known as Dynamic Digital Radiography (DDR) provides a safe and cost-effective method to directly compare changes in motion between patients with rotator cuff tears and correlate these changes to tear severity. The purpose of this study was to evaluate patients with massive and small rotator cuff tears using DDR, assessing for differences in SHR and range of motion between the two groups.Methods:Shoulders were prospectively analyzed using DDR via obtaining a series of pulsed radiographs at up to 15 Hz of the joint in motion. Scapulothoracic motion and glenohumeral motion were quantified based on the DDR images (figure 1). Measurements were taken in humeral abduction at 0-30°, 30-60°, 60-90°, and full abduction using the Grashey view. SHR at different points was calculated by dividing the humeral arc of motion by the scapular arc of motion in each of those motion ranges. Pairwise t-tests were performed on the obtained data to compare differences between groups at an α level of 0.05.Results:42 patients with a primary diagnosis of rotator cuff tear matched for age and BMI were analyzed (table 1). Based on clinical presentation and MRI, 29 patients were classified as massive rotator cuff tear (MRCT) and 13 patients were classified as small rotator cuff tear (SRCT). The final angle calculations are displayed in table 2. SRCT patients had a significantly higher average overall SHR of 3.04 ± 1.16 compared to the MRCT SHR of 1.91 ± 0.51 (p=0.003). When analyzed across 30-degree intervals of humeral abduction, MRCT patients had a significantly lower SHR compared to SRCT patients at 30-60° (1.83 ± 0.97 vs 3.39 ± 1.62, p=0.005). MRCT also had a lower SHR between 0-30° (2.63 ± 2.08 vs 4.74 ± 3.83, p=0.08) and 60-90° (2.13± 0.80 vs 2.91 ± 1.41 , p=0.13) of humeral abduction compared to SRCT patients, but these differences were not statistically significant. SHR changes across these intervals of humeral abduction are plotted in figure 2. Scapular range of motion during humeral abduction was lower in SRCT compared to MRCT (29.71 ± 12.91 vs 41.57 ± 10.41, p=0.08). No statistically significant difference was found in humeral range of motion between MRCT (76.02 ± 22.66) and SRCT (80.93 ± 20.71).Conclusions:A dynamic measurement of motion demonstrates that scapulohumeral rhythm is significantly different between patients with small vs. massive rotator cuff tears. Based on DDR analysis, patients had a marked difference in the initiation of motion and through the first 60 degrees of humeral abduction, resulting in a nearly 2x lower SHR in MRCT compared to SRCT. Moreover, evaluation of glenohumeral and scapulothoracic motion during humeral abduction demonstrated that patients with larger tears had an increased reliance on scapular contributions to overall humeral elevation. Dynamic measurement of SHR represents a novel method for evaluating rotator cuff tears, with marked differences noted in the earlier phases of shoulder motion. Further study and validation of the dynamic assessment of SHR has the potential to augment the diagnostic algorithm for rotator cuff tears, and ultimately inform prognostic and functional expectations for both the patient and the physician.Table 1.Patient DemographicsTable 2.Relative contributions by glenohumeral (GH) and scapulotlioracic (ST) joint to shoulder girdle motionFigure 1.Reference lines used for calculation of the humeral and scapular abduction arcs. Measurements were taken in reference to a vertical line. Calculations were made in the neutral position, at initiation of abduction, 30°, 60°, 90° and full abduction.Figure 2.SHR measured at 30-degree intervals of humeral abduction. The * Indicates a statistically significant difference at α = 0.05 in SHR measured between massive rotator cuff tear and small rotator cuff tear patients. P-values from left to right are 0.08 (0-30 °), 0.005 (30-60 °), 0.13 (60-90 °), and 0.003 (total)