Thessaly Graft Index: An Artificial Intelligence-Based Index for the Assessment of Graft Integrity in ACL-Reconstructed Knees.

Abstract

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.

Methods: The cohort study included 24 patients with an isolated ACL injury that had been treated with a hamstring tendon autograft and followed for 1 year. MRI was performed preoperatively and 1 year postoperatively. The clinical and functional evaluations were performed with use of the KT-1000 and with the following patient-reported outcome measures (PROMs): the Knee Injury and Osteoarthritis Outcome Score (KOOS), the International Knee Documentation Committee Subjective Knee Function form (IKDC), the Lysholm score, and the Tegner Activity Scale (TAS). An AI model, based on the YOLOv5 Nano version, was designed to compute the probability of accurately detecting, in the sagittal plane, a healthy ACL (on a percentage scale) and was trained on healthy and injured knees from the KneeMRI dataset. The model was used to assess the integrity of ACL grafts, with a maximum score of 100. The results were compared with the MRI assessment from an independent radiologist and were correlated with PROMs and KT-1000 laxity.

Results: The mean preoperative and postoperative TGI scores were 64.21 ± 8.96 and 82.37 ± 3.53, respectively. A mean increase of 15% in the TGI scores was observed between preoperative and postoperative images. The minimum threshold for TGI to categorize a graft as healthy on the postoperative MRI was 79.21%. Twenty-two grafts were characterized as intact and 2 as reruptured, with postoperative TGI scores of 71% and 42%. The radiologist's assessment was in total agreement with the TGI scores. The correlation of the TGI ranged from moderate to good with the TAS (0.668), IKDC (0.516), Lysholm (0.521), KOOS total (0.594), and KT-1000 (0.561).

Conclusions: The TGI is an AI tool that is able to accurately recognize an ACL graft rupture. Moreover, the TGI correlated with the KT-1000 postoperative values and PROM scores.

Level of evidence: Diagnostic Level IV. See Instructions for Authors for a complete description of levels of evidence.