The diagnostic performance of impacted third molars in the mandible: A review of deep learning on panoramic radiographs

Background: Mandibular third molar is prone to impaction, resulting in its inability to erupt into the oral cavity.
The radiographic examination is required to support the odontectomy of impacted teeth. The use of computeraided diagnosis based on deep learning is emerging in the field of medical and dentistry with the advancement of
artificial intelligence (AI) technology. This review describes the performance and prospects of deep learning for
the detection, classification, and evaluation of third molar-mandibular canal relationships on panoramic
radiographs.
Methods: This work was conducted using three databases: PubMed, Google Scholar, and Science Direct. Following
the literature selection, 49 articles were reviewed, with the 12 main articles discussed in this review.
Results: Several models of deep learning are currently used for segmentation and classification of third molar
impaction with or without the combination of other techniques. Deep learning has demonstrated significant
diagnostic performance in identifying mandibular impacted third molars (ITM) on panoramic radiographs, with
an accuracy range of 78.91% to 90.23%. Meanwhile, the accuracy of deep learning in determining the rela
tionship between ITM and the mandibular canal (MC) ranges from 72.32% to 99%.
Conclusion: Deep learning-based AI with high performance for the detection, classification, and evaluation of the
relationship of ITM to the MC using panoramic radiographs has been developed over the past decade. However,
deep learning must be improved using large datasets, and the evaluation of diagnostic performance for deep
learning models should be aligned with medical diagnostic test protocols. Future studies involving collaboration
among oral radiologists, clinicians, and computer scientists are required to identify appropriate AI development
models that are accurate, efficient, and applicable to clinical services.