Anterior cruciate ligament (ACL) injuries are prevalent among physically active paediatric and adolescent populations, often necessitating ACL-reconstruction (ACLR) to restore passive knee stability. Complications in the patellofemoral joint (PFJ), such as pain and early osteoarthritis, are common following ACLR. Despite these concerns, post-ACLR PFJ biomechanics remain insufficiently studied. This study aimed to explore the influence of ACLR surgical parameters and subject-specific factors (i.e., knee phenotype, neuromusculoskeletal function) on PFJ biomechanics using an in-silico neuromusculoskeletal (NMSK)-finite element (FE) modeling approach. Three subject-specific NMSK-FE models were used to simulate the effects of four surgical parameters (graft type, size, location, and pre-tension) on PFJ biomechanics (kinematics and cartilage stresses) during walking. Additionally, ACL-deficient (ACLD) models were included to compare PFJ biomechanics in the absence of ACLR. Each surgical combination and ACLD were compared to a corresponding intact knee. Normalized root-mean-square error (nRMSE) quantified deviations in PFJ biomechanics among ACLR, ACLD, and intact knees. PFJ biomechanics in ACLD knees consistently deviated more from intact knees than those in ACLR models, underscoring the restorative effect of reconstruction. Most ACLR surgical combinations restored PFJ kinematics and stress to near intact levels (nRMSE < 10 %) for two participants. In contrast, ∼80.2 % of combinations resulted in substantial deviations (nRMSE > 10 %) for one participant, potentially increasing the risk of cartilage degeneration. Subject-specific factors influenced PFJ outcomes but showed no consistent trends. These findings emphasize the importance of incorporating individualized geometry and loading in simulations to optimize ACLR for biomechanical outcomes. This study provides the first comprehensive evaluation of surgical parameter effects on paediatric PFJ biomechanics following ACLR.
Keywords:
Surgical variability; Precision medicine; Digital twin; Gait analysis; Neuromusculoskeletal