Morphological and topographic evaluation of the mandibular canal and its relationship with the facial profile, skeletal class, and sex

Prado, Gustavo Moraes; Fontenele, Rocharles Cavalcante; Costa, Elizabeth Oliveira da; Freitas, Deborah Queiroz; Oliveira, Matheus Lima

doi:10.1007/s10006-022-01058-x

Cited by 1 publication

(1 citation statement)

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“…In addition, while the anteroposterior dimension was determined by the single measurement of ANB, the vertical dimension was classified using seven cephalometric measurements. The use of several measurements was necessary because it was impossible to classify patients with only one cephalometric measurement [ 44 , 45 ]. As seen in Figure 6 , the feature importance was different for each measurement, which was determined by the training data.…”

Section: Discussionmentioning

confidence: 99%

Classification of Maxillofacial Morphology by Artificial Intelligence Using Cephalometric Analysis Measurements

Ueda,

Tussie,

Kim

et al. 2023

Diagnostics

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The characteristics of maxillofacial morphology play a major role in orthodontic diagnosis and treatment planning. While Sassouni’s classification scheme outlines different categories of maxillofacial morphology, there is no standardized approach to assigning these classifications to patients. This study aimed to create an artificial intelligence (AI) model that uses cephalometric analysis measurements to accurately classify maxillofacial morphology, allowing for the standardization of maxillofacial morphology classification. This study used the initial cephalograms of 220 patients aged 18 years or older. Three orthodontists classified the maxillofacial morphologies of 220 patients using eight measurements as the accurate classification. Using these eight cephalometric measurement points and the subject’s gender as input features, a random forest classifier from the Python sci-kit learning package was trained and tested with a k-fold split of five to determine orthodontic classification; distinct models were created for horizontal-only, vertical-only, and combined maxillofacial morphology classification. The accuracy of the combined facial classification was 0.823 ± 0.060; for anteroposterior-only classification, the accuracy was 0.986 ± 0.011; and for the vertical-only classification, the accuracy was 0.850 ± 0.037. ANB angle had the greatest feature importance at 0.3519. The AI model created in this study accurately classified maxillofacial morphology, but it can be further improved with more learning data input.

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Section: Discussionmentioning

confidence: 99%