Estimating the size of unerupted teeth: Moyers vs deep learning

Abstract

This study aimed to design a deep learning (DL) system for estimating the sum of the mesiodistal widths (MDWs) of unerupted mandibular canines and premolars in the mixed dentition period and to clarify its performance by comparing DL estimates with Moyers' table (MT) results. The training dataset was obtained from 974 patients with permanent dentition. On the 3-dimensional digital models, MDWs of the mandibular right teeth were measured using Ortho Analyzer software (3Shape, Copenhagen, Denmark). A system was designed that could predict the total width of the mandibular canines and premolars using the mandibular central, lateral incisor, and first molar MDWs. This artificial neural system had 5 layers (4 hidden and 1 output) and 886 neurons. The MDWs of the mandibular teeth were introduced to the DL system in the form of datasets. The DL system's predicted results for 100 randomly selected patients were compared with the probability values obtained from the MT. The estimation performance of the DL system for the unerupted mandibular canines and premolars was acceptable, with 49.5% accuracy. The success rate for the MT, in comparison, was 45.0%, with an error margin of 1.00mm. The DL system offers a potential alternative to current methods in estimating unerupted tooth size. The results of the DL system are expected to provide diagnostic support for mixed dentition analysis on dental casts.