Semi-automated technique to assess the developmental stage of mandibular third molars for age estimation

Abstract

Dental age estimation is considered to be an accurate method for identifying the age of an unknown person. This study aims to apply a deep-learning algorithm to assess mandibular third molar development following Demirjian’s method. Demirjian’s classification of tooth development consists of eight stages (Stages A to H). This study has focussed on only the final five stages (Stages D to H), which are found in the 15–23 age range. Eight-hundred mandibular third molar images per stage were cropped manually. In each stage, a total of 720 images were assigned to a training group and the remaining images were assigned to a test group. Automatic developmental stage assessment was performed using the GoogLeNet, a deep-learning algorithm. The overall accuracy of this method was 82.50%, whereas the accuracy in each developmental stage ranged from 87.50% to 97.50%. All of the misinterpreted results of this automatic method revealed only one-stage deviation from the developmental stages assessed by the observer. This developed method revealed a high degree of accuracy. The method can be used in clinical practice as an assistive tool to help clinicians to easily assess the development of mandibular third molars for dental age estimation, reduce time and decrease subjectivity.