Background: Magnetic resonance imaging (MRI) has proven to be a valuable noninvasive tool to evaluate graft integrity after anterior cruciate ligament (ACL) reconstruction. However, MRI protocols and interpretation methodologies are quite diverse, preventing comparisons of signal intensity across subsequent scans and independent investigations. The purpose of this study was to create an artificial intelligence (AI)-based index (Thessaly Graft Index [TGI]) for the evaluation of graft integrity following ACL reconstruction.
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