Comparison of MRI and High-Resolution USG in Evaluating Patients With Rotator Cuff Pathologies
Year: 2024
Type: article
Study design: Observational Study
Abstract: 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.
Source: Cureus
DOI: 10.7759/cureus.72497
Authors: Suchithra S Soundararajan, Shaik Farid
Language: English
Subfield: Surgery
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4403799585