Multi-class Semantic Segmentation of Tooth Pathologies and Anatomical Structures on Bitewing and Periapical Radiographs

Abstract

Detecting dental problems early can prevent invasive procedures and reduce healthcare costs, but traditional exams may not identify all issues, making radiography essential. However, interpreting X-rays can be time-consuming, subjective, prone to error, and requires specialized knowledge. Automated segmentation methods using AI can improve interpretation and aid in diagnosis and patient education. Our U-Net model, trained on 344 bitewing and periapical X-rays, can identify two pathologies and eight anatomical features. It achieves an overall diagnostic performance of 0.794 and 0.787 in terms of Dice score and sensitivity, respectively, 0.493 and 0.405 for dental caries, and 0.471 and 0.44 for root infections. This successful application of deep learning to dental imaging demonstrates the potential of automated segmentation methods for improving accuracy and efficiency in diagnosing and treating dental disorders.