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

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. [ABSTRACT FROM AUTHOR]