RESUMO
OBJECTIVE: In this study, we sought to establish and evaluate an automated workflow to prospectively capture and correlate knee MRI findings with surgical findings in a large medical center. METHODS: This retrospective analysis included data from patients who had undergone knee MRI followed by arthroscopic knee surgery within 6 months during a 2-year period (2019-2020). Discrete data were automatically extracted from a structured knee MRI report template implementing pick lists. Operative findings were recorded discretely by surgeons using a custom-built web-based telephone application. MRI findings were classified as true-positive, true-negative, false-positive, or false-negative for medial meniscus (MM), lateral meniscus (LM), and anterior cruciate ligament (ACL) tears, with arthroscopy used as the reference standard. An automated dashboard displaying up-to-date concordance and individual and group accuracy was enabled for each radiologist. Manual correlation between MRI and operative reports was performed on a random sample of 10% of cases for comparison with automatically derived values. RESULTS: Data from 3,187 patients (1,669 male; mean age, 47 years) were analyzed. Automatic correlation was available for 60% of cases, with an overall MRI diagnostic accuracy of 93% (MM, 92%; LM, 89%; ACL, 98%). In cases reviewed manually, the number of cases that could be correlated with surgery was higher (84%). Concordance between automated and manual review was 99% when both were available (MM, 98%; LM, 100%; ACL, 99%). CONCLUSION: This automated system was able to accurately and continuously assess correlation between imaging and operative findings for a large number of MRI examinations.
