Distinct 3-Dimensional Anatomic Patterns Including Flatter Surfaces and Greater Sagittal Inclinations of Intra-articular Structures Are Reliably Identified Through an Artificial Intelligence-Based Pipeline in Anterior Cruciate Ligament-Injured Knees.

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Abstract

To evaluate whether an automated AI-based pipeline can identify 3-dimensional (3D) anatomic patterns associated with anterior cruciate ligament (ACL) injury from conventional magnetic resonance imaging (MRI) and accurately discriminate ACL-injured from control knees. Retrospective case-control study including 50 patients with ACL rupture and 50 healthy controls. T2-weighted sagittal MRI scans were processed using automated artificial intelligence models to segment cartilage, meniscal, and bone structures, reconstruct 3D models, and extract 19 anatomical variables, including curvatures and inclinations of the tibial plateaus and menisci in three planes. Group comparisons identified significant variables (P < .05), which trained an artificial intelligence classification model (80 training; 20 test set) to differentiate ACL-injured from control knees on previously unseen cases. Patients with ACL rupture exhibited significantly flatter articular surfaces compared with controls: mean curvature of femoral cartilage (0.1277 ± 1.2430 vs -0.5263 ± 1.2328; P = .0061), medial tibial cartilage (0.0623 ± 0.8854 vs -0.3386 ± 0.7525; P = .0253), lateral tibial cartilage (0.1028 ± 0.8562 vs -0.3400 ± 0.7847; P = .0071), and medial meniscus (0.0894 ± 0.9519 vs -0.3583 ± 0.8845; P = .0383). Sagittal inclination was greater in the case group for the lateral plateau (82.73 ± 5.21 vs 79.87 ± 6.09; P = .0019), medial plateau (81.54 ± 5.87 vs 78.90 ± 4.16; P = .0016), lateral meniscus (83.19 ± 4.77 vs 81.83 ± 4.22; P = .0498), and medial meniscus (83.22 ± 4.87 vs 80.67 ± 4.89; P = .0008). Additional significant differences were observed in coronal meniscus-plateau angles (P = .0475 medial, P = .0445 lateral) and sagittal angle between lateral meniscus and plateau (P = .0022). Artificial intelligence model achieved 80% sensitivity, 70% specificity, 75% accuracy and AUC-ROC 0.81 in testing set. Automated 3D MRI analysis revealed distinct anatomical risk patterns in patients with ACL rupture, characterized by flatter articular surfaces and increased sagittal inclination of structures. The exploratory automated 3D MRI pipeline proved feasible for extracting and analyzing combined anatomical features for differentiating ACL injured from control knees. Level III, retrospective case-control study.

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