Role and Correlation of High Resolution Ultrasound and Magnetic Resonance Imaging in Evaluation of Patients with Shoulder Pain

Comparison of MRI and High-Resolution USG in Evaluating Patients With Rotator Cuff Pathologies

Background Accurate diagnosis of rotator cuff pathologies is crucial for the effective management of shoulder pain. This study aimed to evaluate the diagnostic accuracy of high-resolution USG and MRI for detecting rotator cuff injuries. Methods A retrospective study was conducted on 40 patients with shoulder pain who underwent both USG and MRI over six months at the Department of Radiodiagnosis, Karpaga Vinayaga Institute of Medical Sciences and Research Centre in Chennai, India, in collaboration with the Department of Orthopedics. The diagnostic accuracy metrics of USG were compared to those of MRI, considered the reference standard, and analyzed using IBM SPSS Statistics for Windows, Version 28.0 (Released 2021; IBM Corp., Armonk, NY, USA). Results The mean age of the 40 patients was 46.12 ± 15.31 years. Among them, males represented the majority (n = 27, 67.5%), with a significant number having diabetes mellitus (n = 14, 35%) and hypertension (n = 9, 22.5%). The right shoulder was the most frequently affected (n = 33, 82.5%). USG identified supraspinatus tears in 33 patients (82.5%), subscapularis tears in 16 patients (40.0%), and infraspinatus tears in two patients (5.0%). MRI detected supraspinatus tears in 39 patients (97.5%), subscapularis tears in 18 patients (45.0%), and infraspinatus tears in two patients (5.0%). The diagnostic performance of USG demonstrated a sensitivity of 76.92% and specificity of 85.71%, while MRI exhibited a sensitivity of 92.86% and specificity of 80.77%. Conclusions Both USG and MRI are valuable for diagnosing rotator cuff pathologies, and MRI provides superior sensitivity and specificity. However, USG remains a reliable and cost-effective initial diagnostic tool, particularly when used in conjunction with MRI for comprehensive assessment.

Advancements in the diagnosis and management of rotator cuff tears. The role of artificial intelligence

Background

This review examined the role of artificial intelligence (AI) in the diagnosis and management of rotator cuff tears (RCTs).

Methods

A literature search was conducted in October 2023 using PubMed (MEDLINE), SCOPUS, and EMBASE databases, included only peer-reviewed studies. Relevant articles on AI technology in RCTs. A critical analysis of the relevant literature was conducted.

Results

AI is transforming RCTs management through faster and more precise identification and assessment using algorithms that facilitate segmentation, quantification, and classification of the RCTs across various imaging modalities. Precise algorithms focusing on preoperative factors to assess RCTs reparability have been developed for personalized treatment planning and outcome prediction. AI also aids in exercise classification and promotes patient adherence during at-home physiotherapy. Despite promising advancements, challenges in data quality and symptom integration persist. Future research should include refining AI algorithms, expanding their integration into various imaging techniques, and exploring their roles in postoperative care and surgical decision-making.

Conclusions

AI-driven solutions improve diagnostic accuracy and have the potential to influence treatment planning and postoperative outcomes through the automated RCTs analysis of medical imaging. Integration of high-quality datasets and clinical symptoms into AI models can enhance their reliability. Current AI algorithms can also be refined, integrated into other imaging techniques, and explored further in surgical decision-making and postoperative care.

Ultrasound Versus Magnetic Resonance Imaging in the Evaluation of Shoulder Pain in End Stage Renal Disease Patients on Chronic Hemodialysis

BACKGROUND

Musculoskeletal pain is common in hemodialysis (HD) patients and may be related to articular or periarticular amyloid deposition. The shoulder is one of the most common afflicted joints, but not all causes of shoulder pain are detectable on radiography, and magnetic resonance imaging (MRI) is not always available. The aim of this study was to evaluate the validity of musculoskeletal ultrasound (MSUS) to properly detect shoulder disorders in HD patients by identifying US abnormalities in the shoulder and comparing them to those identified on MRI, with MRI serving as the gold standard test.

METHODS

This cross-sectional observational study was conducted on 28 HD patients (16 males and 12 females, mean age 46.89) with either unilateral or bilateral shoulder pain. Demographic data and clinical characteristics were recruited. All patients were subjected to clinical assessment, MSUS and MRI of both shoulders.

RESULTS

US abnormalities were prevalent in almost all patients. Supraspinatus tendinopathy was the most common abnormality in symptomatic shoulders (92.1%), followed by subacromial-subdeltoid (SASD) bursitis (65.8%), humoral erosions (57.9%), and acromioclavicular joint (ACJ) osteoarthritis (52.6%). MSUS shows high sensitivity and specificity when compared to MRI in all the studied shoulder pathologies except glenohumeral joint (GHJ) effusion (sensitivity, 33.3%) and infraspinatus tendinopathy (sensitivity, 58.3%). The percentage of agreement between MSUS and MRI in detecting biceps tenosynovitis was 82.14% (kappa, 0.64), subscapularis tendinopathy 83.93% (kappa, 0.654), supraspinatus tendinopathy 91.07% (kappa, 0.617), infraspinatus tendinopathy 82.14% (kappa, 0.470), SASD bursitis 80.36% (kappa, 0.569), humeral head erosions 82.14% (kappa, 0.635), GHJ effusion 82.14% (kappa, 0.352), and ACJ osteoarthritis 76.79% (kappa, 0.539).

CONCLUSIONS

Shoulder problems are common in HD patients, even in people who do not have obvious shoulder complaints. MSUS is a valuable imaging technique that assists in the diagnosis of HD patients who report shoulder pain.

Validation of a new classification for periprosthetic shoulder fractures

INTRODUCTION

Successful treatment of periprosthetic shoulder fractures depends on the right strategy, starting with a well-structured classification of the fracture. Unfortunately, clinically relevant factors for treatment planning are missing in the pre-existing classifications. Therefore, the aim of the present study was to describe a new specific classification system for periprosthetic shoulder fractures including a structured treatment algorithm for this important fragility fracture issue.

METHODS

The classification was established, focussing on five relevant items, naming the prosthesis type, the fracture localisation, the rotator cuff status, the anatomical fracture region and the stability of the implant. After considering each single item, the individual treatment concept can be assessed in one last step. To evaluate the introduced classification, a retrospective analysis of pre- and post-operative data of patients, treated with periprosthetic shoulder fractures, was conducted by two board certified trauma surgery consultants.

RESULTS

The data of 19 patients (8 male, 11 female) with a mean age of 74 ± five years have been analysed in our study. The suggested treatment algorithm was proven to be reliable, detected by good clinical outcome in 15 of 16 (94%) cases, where the suggested treatment was maintained. Only one case resulted in poor outcome due to post-operative wound infection and had to be revised.

CONCLUSIONS

The newly developed six-step classification is easy to utilise and extends the pre-existing classification systems in terms of clinically-relevant information. This classification should serve as a simple tool for the surgeon to consider the optimal treatment for his patients.