MRI Predictors Of Meniscal And ACL Tears In Knee Trauma: A Retrospective Cross-Sectional Study
DOI:
https://doi.org/10.53555/re2evy95Keywords:
Magnetic resonance imaging, meniscal tears, anterior cruciate ligament, knee trauma, diagnostic accuracyAbstract
Knee injuries involving meniscal and anterior cruciate ligament tears represent a significant burden in orthopedic practice, particularly in young adults. Accurate preoperative diagnosis through magnetic resonance imaging can substantially influence surgical planning and patient outcomes.
Objectives: To evaluate the diagnostic accuracy of MRI findings in predicting meniscal and ACL tears in patients presenting with knee trauma, and to identify specific imaging parameters that demonstrate highest sensitivity and specificity.
Methodology: This retrospective cross-sectional study was conducted at M M Institute of Medical Sciences and Research, Mullana, Ambala, between July and December 2007. Medical records of 184 patients who underwent knee MRI followed by arthroscopy were reviewed. MRI findings including signal intensity changes, morphological alterations, and associated bone marrow edema patterns were correlated with arthroscopic findings considered as gold standard. Data analysis utilized SPSS version 14.0 with chi-square tests and calculation of sensitivity, specificity, and diagnostic accuracy.
Results: Among 184 patients, meniscal tears were arthroscopically confirmed in 126 cases (68.5%) while ACL tears were identified in 89 cases (48.4%). MRI demonstrated sensitivity of 91.3% and specificity of 86.2% for meniscal tears. For ACL tears, sensitivity reached 94.4% with specificity of 88.4%. Bone marrow edema pattern showed strong association with acute ACL injuries (p<0.001). Combined assessment of multiple MRI parameters improved overall diagnostic accuracy to 89.7%.
Conclusion: MRI proves highly reliable for preoperative assessment of meniscal and ACL pathology in knee trauma patients. Integration of multiple imaging criteria enhances diagnostic precision and facilitates appropriate management decisions in resource-limited settings.
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