Forensic science international · 2017
Use of automated learning techniques for predicting mandibular morphology in skeletal class I, II and III.
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Niño-Sandoval Tania Camila, Guevara Pérez Sonia V, González Fabio A, Jaque Robinson Andrés, Infante-Contreras Clementina
Аннотация
BACKGROUND: The prediction of the mandibular bone morphology in facial reconstruction for forensic purposes is usually performed considering a straight profile corresponding to skeletal class I, with application of linear and parametric analysis which limit the search for relationships between mandibular and craniomaxillary variables.
OBJECTIVE: To predict the mandibular morphology through craniomaxillary variables on lateral radiographs in patients with skeletal class I, II and III, using automated learning techniques, such as Artificial Neural Networks and Support Vector Regression.
MATERIALS AND METHODS: 229 standardized lateral radiographs from Colombian patients of both sexes aged 18-25 years were collected. Coordinates of craniofacial landmarks were used to create mandibular and craniomaxillary variables. Mandibular measurements were selected to be predicted from 5 sets of craniomaxillary variables or input characteristics by using automated learning techniques, and they were evaluated through a correlation coefficient by a ridge regression between the real value and the predicted value.
RESULTS: Coefficients from 0.84 until 0.99 were obtained with Artificial Neural Networks in the 17 mandibular measures, and two coefficients above 0.7 were obtained with the Support Vector Regression.
CONCLUSION: The craniomaxillary variables used, showed a high predictability ability of the selected mandibular variables, this may be the key to facial reconstruction from specific craniomaxillary measures in the three skeletal classifications.
OBJECTIVE: To predict the mandibular morphology through craniomaxillary variables on lateral radiographs in patients with skeletal class I, II and III, using automated learning techniques, such as Artificial Neural Networks and Support Vector Regression.
MATERIALS AND METHODS: 229 standardized lateral radiographs from Colombian patients of both sexes aged 18-25 years were collected. Coordinates of craniofacial landmarks were used to create mandibular and craniomaxillary variables. Mandibular measurements were selected to be predicted from 5 sets of craniomaxillary variables or input characteristics by using automated learning techniques, and they were evaluated through a correlation coefficient by a ridge regression between the real value and the predicted value.
RESULTS: Coefficients from 0.84 until 0.99 were obtained with Artificial Neural Networks in the 17 mandibular measures, and two coefficients above 0.7 were obtained with the Support Vector Regression.
CONCLUSION: The craniomaxillary variables used, showed a high predictability ability of the selected mandibular variables, this may be the key to facial reconstruction from specific craniomaxillary measures in the three skeletal classifications.