Machine Learning for the Classification of Surgical Patients in Orthodontics

Abstract

Dentofacial anomalies, also known as malocclusions, are alterations with a congenital, traumatic, or growth origin. These anomalies can generate functional and aesthetic problems in those who suffer from them and have been reported by the World Health Organization as the third most prevalent oral disease. The most commonly used methods for correcting these anomalies are orthodontics and orthognathic surgery. The diagnosis, and the correct selection of the treatment to be carried out, are part of an extensive process that involves collecting different cephalometric and clinical data, and depend on the clinician's experience. Therefore, no standardized process allows the classification or diagnosis among patients who achieve the best result with orthodontics, that is, non-surgical procedures or if surgical intervention is necessary. This study aims to propose a digital tool based on machine learning algorithms that may help the clinician to select an orthodontics or surgical treatment for patients who are about to start their treatment.