Tania Camila Niño Sandoval

Sonia Victoria Guevara Pérez

Fabio Augusto González

Robinson Andrés Jaque

Clementina Infante Contreras


Background: Predicting mandibular morphology is important in facial reconstruction for forensic purposes as in orthodontics and maxillofacial surgery. This process has been performed through parametric and linear methods based on Caucasian populations. Also, these analyzes are performed on lateral cephalograms, but a prediction from a posteroanterior view is not taken into account. Purpose: To predict through artificial neural networks the mandibular morphology using craniomaxillary measures in posteroanterior radiographs. Methods: 229 standardized posteroanterior radiographs from Colombian young adults of both sexes were collected. Coordinates of craniofacial skeletal landmarks were used to create mandibular and craniomaxillary measures. 17 predictor craniomaxillary input variables were selected, measuring widths, heights, and angles. Similarly, 13 mandibular measures were selected to be predicted, considering both the right and left sides. Artificial neural networks were used for the prediction process and it was evaluated by a correlation coefficient using a ridge regression between real value and the predicted value. Results: The results found in the model were significant especially for 5 variables of morphological importance in the forensic field: right mandibular ramus (Cdd-God), bigonial width (Goi-God), bicondylar width (Cdi-Cdd), and distance between the condyles to the menton (Cdd-Me and Cdi-Me). Conclusions: An important prediction capacity in 5 measures of forensic importance in patients with skeletal Class I, Class II and Class III was found in both sexes.


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How to Cite
Niño Sandoval, T., Guevara Pérez, S., González, F., Jaque, R., & Infante Contreras, C. (2016). Use of Artificial Neural Networks for Mandibular Morphology Prediction through Craniomaxillar Variables. Universitas Odontologica, 35(74), 21-28. https://doi.org/10.11144/Javeriana.uo35-74.urna
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