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

Abstract

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.

Keywords: panoramic radiography; artificial intelligence,; deep learning; mandibular first molar; root morphology.

Figures

Figure 1.

Methods for the reconstruction of a CBCT image The reconstructed coronal image displays the tooth in a bucco-lingual direction, and indicates an extra root in the distal root. D, distal; M, mesial; L, Lingual; B, Buccal.

Figure 2.

Segmentation of the mandibular first molar on a panoramic radiograph A single radiologist segmented each of the mesial and distal roots of the mandibular first molars using an image patch size of 70 × 120-pixels.

Figure 3.

Five-fold cross-validation The data were randomly split into five partitions, with one partition being used as a testing set and the residual data being used as a training sample. A learning model was created on the basis of the training sample, and the testing data were then applied to each model. The diagnostic performance for each cross-validation set was obtained, and the average of the fivefold procedure was regarded as the estimated performance.

Figure 4.

Receiver operating characteristic curves for the deep learning system and the radiologists