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.
Spinal and neck injuries are major biomechanical concerns during pilot ejection, particularly under high vertical acceleration. As the main load-transfer interface between the seat pan and the occupant, the cushion affects how impact loads are transmitted to the body. This study evaluated a dual-layer cushion consisting of an N3 slow-recovery contact layer and an N1 high-rebound support layer at a thickness ratio of 2:1, with a single-layer N1 cushion as the reference. Full-scale vertical dummy ejection tests at 10 G and 14 G were used to assess dynamic responses of the spine and neck. A CT-based human-cushion finite element model was also used to analyse static interface pressure and local spinal responses under a representative high-acceleration condition. Compared with the reference cushion, the dual-layer cushion reduced by 17.6% at 10 G and 7.3% at 14 G, while also reducing the resultant forces and values in the upper and lower neck. At 14 G, upper- and lower-neck decreased by 19.9% and 27.6%, respectively. The finite element results further showed reductions of 13.1% and 14.5% in peak stress at the L4-L5 and L5-S1 intervertebral discs, respectively. These results indicate that layered cushion materials can alter impact-load transmission at the human-seat interface and reduce biomechanical responses associated with spinal and neck injury. The dummy tests and human finite element analysis provide complementary evidence at the whole-body dynamic and local tissue-mechanical levels for evaluating the biomechanical protection of the dual-layer cushion.
Subject-specific finite element (FE) human body models are valuable for biomechanics, injury analysis, and personalized medical planning; however, generating such models efficiently while maintaining mesh quality and solver compatibility remains challenging. This study presents an integrated framework for rapid FE human body model morphing with three key features. First, the framework uses automatic multi-source landmark generation, combining surface correspondences, extremal points, curvature-based landmarks, edge or boundary points, and FE-proximity anchors to improve anatomical coverage and deformation control. Second, it incorporates landmark down-sampling before nonrigid radial basis function (RBF) morphing to reduce computational cost while preserving a spatially distributed set of control points. Third, it directly updates the nodal coordinates of the original FE model, thereby preserving element connectivity, part definitions, material assignments, contacts, boundary conditions, and other solver keyword structures. The framework was demonstrated using two model-to-model morphing cases based on existing FE human body model geometries rather than CT- or MRI-derived subject anatomy: morphing a THUMS 10YO thoracic spine toward CHARM-10 geometry and morphing a VIVA+ F50 torso toward VIVA+ M50 geometry. The morphed models showed improved geometric agreement with the target surfaces and remained numerically stable in spine torsion and sternum impact simulations, which were used as solver-readiness checks rather than full biomechanical validation. Runtime analysis showed that landmark alignment is the main computational bottleneck, and reducing landmark density before alignment can substantially lower computational cost, with a trade-off in geometric accuracy. These results demonstrate a promising solver-compatible FE morphing framework at regional and torso scales. Future work will evaluate additional anatomies, postures, mesh densities, model families, full-body models, and patient-derived geometries to fully validate the proposed framework as a general solution for subject-specific FE model generation.
PMMA hip spacers used in two-stage revision surgery are prone to brittle fracture due to their low fracture toughness and the presence of defects that can initiate crack growth, motivating the need to assess the effectiveness of metallic reinforcement under quasi-static loading conditions. This study evaluates the fracture behavior of reinforced and unreinforced hip spacers using a validated explicit finite element model combined with a sub-modeling approach, in which a spacer embedded in a femoral cavity is simulated and a quarter-elliptical corner crack is introduced in the most highly stressed region to compute stress intensity factors for Modes I, II, and III across varying crack sizes and reinforcement thicknesses. The numerical predictions showed good agreement with experimental load-displacement results, confirming the validity of the model. For unreinforced spacers, Mode I stress intensity factors exceeded the fracture toughness of PMMA for all crack sizes, indicating a high risk of crack propagation and fracture, with Mode I identified as the dominant failure mechanism while Modes II and III contributed minimally. The incorporation of titanium reinforcement significantly reduced stress intensity factors, achieving reductions exceeding 85% for 6 mm reinforcement and further improvement for 8 mm reinforcement, while increasing thickness to 10 mm produced only limited additional benefit. Overall, reinforcement enabled the spacer to withstand loads representative of daily activities, demonstrating that unreinforced spacers are highly susceptible to fracture whereas reinforced designs substantially improve structural integrity, with an 8 mm titanium reinforcement providing an optimal balance between mechanical strength and efficiency.
Surrogate-assisted optimization can reduce the cost of exploring ventricular-assist-device (VAD) rotor geometries, but the optimization variables, physical constraints, and uncertainty of the surrogate predictions must be treated consistently. This study analyzes a CFD-derived design space of 4809 axial-flow VAD rotors defined by six geometric variables at 5 L/min and 10,000 rpm. Seven archived responses were modeled using output-specific Random Forest, Extra Trees, Gradient Boosting, and radial-basis-function support-vector regressors. Independent test-set R 2 values ranged from 0.964 to 0.994. Hydraulic efficiency and the archived exposure-time output were retained as diagnostic responses but excluded from optimization because consistency audits did not support their use as independent physical objectives. A constraint-aware multi-objective particle swarm optimization therefore minimized torque, hemolysis index, and platelet activation state and maximized the volume fraction below 50 Pa, while requiring pressure rise to meet the baseline target. Five runs with independent seeds were combined through their best-hypervolume archives, and five diverse Pareto candidates were re-evaluated by direct CFD. The final CFD-confirmed compromise used four blades, a 52.18° inlet angle, a 25.00° outlet angle, a 0.105 mm clearance, a 0.600 mm blade thickness, and a 38.30 mm rotor length. Relative to the baseline, direct CFD maintained pressure rise (+0.15%), reduced torque by 6.77%, increased the low-shear volume fraction by 4.14%, and reduced the comparative hemolysis and platelet-activation indicators by 36.07% and 26.38%, respectively. The results establish a reproducible fixed-operating-point design-screening workflow, while absolute blood-damage interpretation and operation beyond the prescribed point require further validation.
