Development and validation of a graph convolutional network (GCN)-based automatic superimposition method for maxillary digital dental models (MDMs).

Objectives: To validate the accuracy and reliability of a graph convolutional network (GCN)-based superimposition method of a maxillary digital dental model (MDM) by comparing it with manual superimposition and quantifying the clinical error from this method.

Materials and methods: Based on a GCN, learning the features from 100 three-dimensional digital occlusal models under supervision of the palatal stable structure labels that were manually annotated by senior specialists, the palatal stable structure was automatically segmented. The average Hausdorff distance was calculated to assess the difference between automatic and manual segmentations. Tooth position and angulation, including rotation, tip, and torque, of bilateral upper first molars and central incisors were obtained to measure the clinical error of automatic superimposition. Reliability was calculated by intraclass correlation coefficient (ICC).

Results: The average Hausdorff distance was 0.36 mm between automatic and manual segmentations of the palatal stable region and was larger than the intraexaminer and interexaminer deviations. The tooth position deviation was <0.32 mm, and the tooth angulation difference was <0.26° for tip and torque, and 0.46-0.61° in rotation. ICCs, used for assessment of reliability, ranged from 0.82 to 0.99 in all variables.

Conclusions: The GCN-based MDM superimposition is an efficient method for the assessment of tooth movement in adults. The clinical error in tooth position and angulation induced by the method was clinically acceptable. Reliability was as high as manual segmentation.