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Mandibular shape prediction model using machine learning techniques.

Tania Camila Niño-Sandoval¹, Robinson Andrés Jaque², Fabio A González²

¹Department of Oral and Maxillofacial Surgery and Traumatology, Postgraduate Program in Oral and Maxillofacial Surgery and Traumatology. Universidade de Pernambuco - School of Dentistry (UPE/FOP), University Hospital Oswaldo Cruz, Rua Arnóbio Marquês, 310 - Santo Amaro, CEP: 50.100-130, Recife, PE, Brazil.

Clinical Oral Investigations

|January 8, 2022

Summary

A new machine learning model accurately predicts mandibular shape using geometric morphometrics and craniomaxillary variables. This economical method shows promise for clinical applications in the Latin American population.

Keywords:

Mandibular shape Prediction Support vector machines Three-dimensional geometric morphometrics

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Area of Science:

Biomedical Engineering
Computer Science
Anthropology

Background:

Accurate prediction of mandibular shape is crucial for various applications, including orthodontics and reconstructive surgery.
Traditional methods for mandibular shape analysis can be time-consuming and resource-intensive.

Purpose of the Study:

To develop and validate a machine learning model for predicting three-dimensional mandibular shape.
To explore the covariation between craniomaxillary variables and mandibular shape components.

Main Methods:

Utilized support vector machines to identify key craniomaxillary variables from 629 radiographs.
Employed Procrustes fit and 3D landmark digitization on 55 tomograms to define mandibular shape.
Applied partial least square regression to model the relationship between craniofacial angles and mandibular shape.

Main Results:

Achieved 88.66% covariation between craniomaxillary variables and mandibular shape, with high statistical significance.
The prediction model demonstrated a low mean absolute error of 0.0143.
Validated the model's accuracy using Hausdorff distance, showing high similarity between predicted and actual mandibular surfaces.

Conclusions:

The developed machine learning approach offers a promising, economical method for mandibular shape prediction.
The model is applicable to the Latin American population and presents a reliable alternative to traditional techniques.
Further studies with larger sample sizes are recommended for clinical validation.