Abstract
<jats:p>Obstructive sleep apnea (OSA) is commonly treated using mandibular advancement devices (MADs), but designs that improve upperairway patency may also increase appliance loading and alter force transfer to dentoalveolar structures. This study developed a casespecific computational framework for MAD design and multi-objective optimization by integrating parametric computer-aided design,finite element analysis (FEA), computational fluid dynamics (CFD), design-of-experiments sampling, surrogate modeling, and Paretooptimization.A three-dimensional anatomical model was reconstructed from a publicly available cone-beam computed tomography volume. A two-pieceMAD was parameterized using six design variables: mandibular advancement, vertical opening, guiding-block thickness, splint thickness,sagittal mandibular rotation, and material elastic modulus. A total of 120 geometrically feasible configurations were evaluated using FEAand CFD. Separate XGBoost and feedforward artificial neural-network surrogate models were trained for the simulated biomechanical andaerodynamic responses. The validated models for maximum MAD von Mises stress, airway pressure drop, and minimum cross-sectionalairway area were coupled with the non-dominated sorting genetic algorithm II (NSGA-II). Tooth stress, periodontal-ligament strain, toothdisplacement, and MAD deformation were retained as secondary outputs and did not affect Pareto ranking.The selected surrogate models achieved external-test coefficients of determination ranging from 0.91 to 0.96. The optimization identified53 unique Pareto-optimal configurations. The selected compromise design comprised a mandibular advancement of 6.4 mm, verticalopening of 3.2 mm, guiding-block thickness of 4.1 mm, splint thickness of 2.6 mm, sagittal rotation of 10.8°, and material elastic modulusof 1250 MPa. Relative to a central reference configuration, the compromise design reduced maximum MAD von Mises stress by 19.4%,airway pressure drop by 16.3%, and 99th-percentile airflow velocity by 6.3%, while increasing the minimum cross-sectional airway area by8.3%. High-fidelity verification yielded surrogate-prediction errors of 1.8%, 1.9%, and 1.3% for MAD stress, pressure drop, and minimumcross-sectional airway area, respectively.The proposed framework enables computationally efficient screening of competing appliance-level structural and aerodynamic objectivesbefore physical prototyping. However, the findings represent a single-case, anatomy-based computational proof of concept. Because theselected public CBCT case was not clinically characterized for OSA and lacked polysomnographic and treatment-response data, the resultsshould not be interpreted as patient-specific predictions of disease severity, therapeutic response, or clinical outcome.</jats:p>