Novel AI-based tool for primary tooth segmentation on CBCT using convolutional neural networks: A validation study

Abstract

Background: Primary teeth segmentation on cone beam computed tomography (CBCT) scans is essential for paediatric treatment planning. Conventional methods, however, are time-consuming and necessitate advanced expertise. Aim: The aim of this study was to validate an artificial intelligence (AI) cloud-based platform for automated segmentation (AS) of primary teeth on CBCT. Its accuracy, time efficiency, and consistency were compared with manual segmentation (MS). Design: A dataset comprising 402 primary teeth (37 CBCT scans) was retrospectively retrieved from two CBCT devices. Primary teeth were manually segmented using a cloud-based platform representing the ground truth, whereas AS was performed on the same platform. To assess the AI tool's performance, voxel- and surface-based metrics were employed to compare MS and AS methods. Additionally, segmentation time was recorded for each method, and intra-class correlation coefficient (ICC) assessed consistency between them. Results: AS revealed high performance in segmenting primary teeth with high accuracy (98 ± 1%) and dice similarity coefficient (DSC; 95 ± 2%). Moreover, it was 35 times faster than the manual approach with an average time of 24 s. Both MS and AS demonstrated excellent consistency (ICC = 0.99 and 1, respectively). Conclusion: The platform demonstrated expert-level accuracy, and time-efficient and consistent segmentation of primary teeth on CBCT scans, serving treatment planning in children.