Automatic Impacted Tooth Segmentation Using Artificial Intelligence Model on Cone-Beam Computed Tomography Images

Tooth impaction is a common pathological dental condition. The etiology behind tooth impaction can be systemic, local, or genetic. Some other reasons for impaction are lack of space in the arch and hinderance in the eruption path of the tooth. Different pathologies associated with impacted teeth are pericoronitis, orofacial pain, TMJ disorders, pathological fractures, cysts, and neoplasms. Considering the complexity of the location and pathology associated with impacted teeth, cone-beam computed tomography (CBCT) is considered as imaging of choice for detection of impacted tooth and its relations to surrounding structures. In addition to this, with the idea of reducing the errors in subjective evaluations related to the person, artificial intelligence applications can play a key role in detecting impacted teeth on advanced imaging such as CBCT. The aim of the study is to assess the success of artificial intelligence (AI) model for the automatic impacted tooth segmentation on CBCT images.