Quantifying the biomechanical impact of ligament injury in the upper cervical spine: A finite element–machine learning approach

Upper cervical spine (UCS) instability, often caused by chronic strain, poses a major clinical challenge due to the complex ligamentous architecture governing the biomechanical integrity of the occipitoatlantal (Oc–C1) and atlantoaxial (C1–C2) joints. This study presents a detailed computational framework that combines a high-fidelity finite element model with machine learning (ML) techniques to elucidate the role of specific ligaments in stabilizing the UCS. A 3D FE model of the Oc–C2 segment was created from CT scan data and subjected to physiological moment loads (0.5–1.5 N·m) in multiple anatomical planes. ML algorithms Random Forest Regressor, K -means clustering, and Isolation Forest were employed to rank ligament contributions, classify stress behavior, and detect stress anomalies, respectively. The results revealed that the occipitoatlantal capsular ligament (OACL) has the highest predictive relevance (score: 0.187), followed by the alar ligament (0.085), indicating their prominent roles in craniocervical stability. Isolation Forest identified alar-flexion and OACL-right axial rotation combinations as stress outliers (>1.5 MPa), while K -means clustering placed these ligaments into a high-stress behavioral class. Injury simulations showed that alar ligament injury increased C1–C2 lateral bending range of motion (ROM) by 33%, and OACL removal increased Oc–C1 ROM by up to 15% in extension and axial rotation. These results indicate that the alar ligament and OACL are the primary mechanical stabilizers of the UCS. This study shows that integrating FE modeling with ML analytics yields scalable, data-driven insights into cervical spine biomechanics, supporting advances in trauma assessment, surgical planning, and implant design.