A deep-learning artificial intelligence system for assessment of root morphology of the mandibular first molar on panoramic radiography

Objectives: The distal root of the mandibular first molar occasionally has an extra root, which can directly affect the outcome of endodontic therapy. In this study, we examined the diagnostic performance of a deep learning system for classification of the root morphology of mandibular first molars on panoramic radiographs. Dental cone-beam CT (CBCT) was used as the gold standard. Methods: CBCT images and panoramic radiographs of 760 mandibular first molars from 400 patients who had not undergone root canal treatments were analyzed. Distal roots were examined on CBCT images to determine the presence of a single or extra root. Image patches of the roots were segmented from panoramic radiographs and applied to a deep learning system, and its diagnostic performance in the classification of root morphplogy was examined. Results: Extra roots were observed in 21.4% of distal roots on CBCT images. The deep learning system had diagnostic accuracy of 86.9% for the determination of whether distal roots were single or had extra roots. Conclusions: The deep learning system showed high accuracy in the differential diagnosis of a single or extra root in the distal roots of mandibular first molars.