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

SUBMITTER: Elsonbaty S 

PROVIDER: S-EPMC11626492 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature