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In cases of osseous defects, knowledge of the anatomy, and its age and sex-related variations, is essential for reconstruction of normal morphology. Here, we aimed at creating a 3D atlas of the human mandible in an adult sample using dense landmarking and geometric morphometrics. We segmented 50 male and 50 female mandibular surfaces from CBCT images (age range: 18.9–73.7 years). Nine fixed landmarks and 510 sliding semilandmarks were digitized on the mandibular surface, and then slid by minimizing bending energy against the average shape. Principal component analysis extracted the main patterns of shape variation. Sexes were compared with permutation tests and allometry was assessed by regressing on the log of the centroid size. Almost 49 percent of shape variation was described by the first three principal components. Shape variation was related to width, height and length proportions, variation of the angle between ramus and corpus, height of the coronoid process and inclination of the symphysis. Significant sex differences were detected, both in size and shape. Males were larger than females, had a higher ramus, more pronounced gonial angle, larger inter-gonial width, and more distinct antegonial notch. Accuracy of sexing based on the first two principal components in form space was 91 percent. The degree of edentulism was weakly related to mandibular shape. Age effects were not significant. The resulting atlas provides a dense description of mandibular form that can be used clinically as a guide for planning surgical reconstruction.
In cases of osseous defects, knowledge of the anatomy, and its age and sex-related variations, is essential for reconstruction of normal morphology. Here, we aimed at creating a 3D atlas of the human mandible in an adult sample using dense landmarking and geometric morphometrics. We segmented 50 male and 50 female mandibular surfaces from CBCT images (age range: 18.9–73.7 years). Nine fixed landmarks and 510 sliding semilandmarks were digitized on the mandibular surface, and then slid by minimizing bending energy against the average shape. Principal component analysis extracted the main patterns of shape variation. Sexes were compared with permutation tests and allometry was assessed by regressing on the log of the centroid size. Almost 49 percent of shape variation was described by the first three principal components. Shape variation was related to width, height and length proportions, variation of the angle between ramus and corpus, height of the coronoid process and inclination of the symphysis. Significant sex differences were detected, both in size and shape. Males were larger than females, had a higher ramus, more pronounced gonial angle, larger inter-gonial width, and more distinct antegonial notch. Accuracy of sexing based on the first two principal components in form space was 91 percent. The degree of edentulism was weakly related to mandibular shape. Age effects were not significant. The resulting atlas provides a dense description of mandibular form that can be used clinically as a guide for planning surgical reconstruction.
Patient-specific 3D models of the human mandible are finding increasing utility in medical fields such as oral and maxillofacial surgery, orthodontics, dentistry, and forensic sciences. The efficient creation of personalized 3D bone models poses a key challenge in these applications. Existing solutions often rely on 3D statistical models of human bone, offering advantages in rapid bone geometry adaptation and flexibility by capturing a range of anatomical variations, but also a disadvantage in terms of reduced precision in representing specific shapes. Considering this, the proposed parametric model allows for precise manipulation using morphometric parameters acquired from medical images. This paper highlights the significance of employing the parametric model in the creation of a personalized bone model, exemplified through a case study targeting mandibular prognathism reconstruction. A personalized model is described as 3D point cloud determined through the utilization of series of parametric functions, determined by the application of geometrical morphometrics, morphology properties, and artificial neural networks in the input dataset of human mandible samples. With 95.05% of the personalized model’s surface area displaying deviations within −1.00–1.00 mm relative to the input polygonal model, and a maximum deviation of 2.52 mm, this research accentuates the benefits of the parametric approach, particularly in the preoperative planning of mandibular deformity surgeries.
Accurate assessment and prediction of mandible shape are fundamental prerequisites for successful orthognathic surgery. Previous studies have predominantly used linear models to predict lower facial structures from facial landmarks or measurements; the prediction errors for this did not meet clinical tolerances. This paper compared non-linear models, namely a Multilayer Perceptron (MLP), a Mixture Density Network (MDN), and a Random Forest (RF) model, with a Linear Regression (LR) model in an attempt to improve prediction accuracy. The models were fitted to a dataset of measurements from 155 subjects. The test-set mean absolute errors (MAEs) for distance-based target features for the MLP, MDN, RF, and LR models were respectively 2.77 mm, 2.79 mm, 2.95 mm, and 2.91 mm. Similarly, the MAEs for angle-based features were 3.09°, 3.11°, 3.07°, and 3.12° for each model, respectively. All models had comparable performance, with neural network-based methods having marginally fewer errors outside of clinical specifications. Therefore, while non-linear methods have the potential to outperform linear models in the prediction of lower facial measurements from upper facial measurements, current results suggest that further refinement is necessary prior to clinical use.
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