Artificial intelligence-aided detection of ectopic eruption of maxillary first molars based on panoramic radiographs

Abstract

ObjectivesEctopic eruption (EE) of maxillary permanent first molars (PFMs) is among the most frequent ectopic eruption, which leads to premature loss of adjacent primary second molars, impaction of premolars and a decrease in dental arch length. Apart from oral manifestations such asdelayed eruption of PFMs and discoloration of primary second molars, panoramic radiographs can reveal EE of maxillary PFMs as well. Identifying eruption anomalies in radiographs can be strongly experience-dependent, leading us to develop here an automatic model that can aid dentists in this task and allow timelier interventions. MethodsPanoramic X-ray images from 1480 patients aged 4-9 years old were used to train an auto-screening model. Another 100 panoramic images were used to validate and test the model. ResultsThe positive and negative predictive values of this auto-screening system were 0.86 and 0.88, respectively, with a specificity of 0.90 and a sensitivity of 0.86. Using the model to aid dentists in detecting EE on the 100 panoramic images led to higher sensitivity and specificity than when three experienced pediatric dentists detected EE manually. ConclusionsDeep learning-based automatic screening system is useful and promising in the detection EE of maxillary PFMs with relatively high specificity. However, deep learning is not completely accurate in the detection of EE. To minimize the effect of possible false negative diagnosis, regular follow-ups and re-evaluation are required if necessary. Clinical significanceIdentification of EE through a semi-automatic screening model can improve the efficacy and accuracy of clinical diagnosis compared to human experts alone. This method may allow earlier detection and timelier intervention and management.