Abstract:

Objective To establish a  high-precision three-dimensional statistical shape model （SSM） of the mandible based on cranial CT data from a Chinese population， and to explore its clinical applications in morphological analysis， automated reconstruction of mandibular defects， and prediction of mandibular morphology based on facial shape. Methods Cranial CT data from 130 healthy Han Chinese adults were collected， of which 100 cases were used for model training and 30 for validation. Three-dimensional reconstruction， standardization， and non-rigid registration were performed using Mimics and Meshmixer to ensure topological consistency across models， followed by singular value decomposition （SVD） to construct the SSM. Principal component analysis （PCA） was employed to extract morphological features and compare differences between sexes. A posterior shape model （PSM） algorithm was integrated to achieve automated reconstruction of mandibular defects. In addition， leveraging the correlation between a three-dimensional facial SSM and the mandibular SSM， a support vector regression （SVR） model was developed to predict mandibular morphology based on facial shape. Results The first 15 principal components of the mandibular SSM explained 95.18% of shape variance. The average fitting error on the test set was  （0.55±0.08） mm， indicating strong generalization. Sex-related differences were primarily observed in overall volume and gonial angle characteristics. PSM-based automated reconstruction achieved higher accuracy than manual reconstruction in angle and body defects （RMSE of 0.77 mm and 0.67 mm； Dice coefficients of 0.87 and 0.94， respectively）. The mean error for predicting mandibular morphology from three-dimensional facial shape was （1.50±0.34） mm， significantly lower than inter-individual differences in a random population of （2.58±0.98） mm （P<0.001）. Conclusion This study presents the first high-precision， strongly generalizable three-dimensional mandibular SSM tailored to a Chinese population， and demonstrates its clinical feasibility for morphological analysis， automated defect reconstruction， and facial-shape-based prediction of mandibular morphology. The model provides new technical support and a research foundation for personalized maxillofacial surgical planning， radiation-free assessment， and telemedicine.

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