Progress of artificial intelligence-driven solutions for automated segmentation of dental pulp cavity on cone-beam computed tomography images. A systematic review

 Journal of Endodontics

Published:May 29, 2024DOI:



Automated segmentation of three-dimensional pulp space on cone-beam computed tomography (CBCT) images presents a significant opportunity for enhancing diagnosis, treatment planning, and clinical education in endodontics. The aim of this systematic review was to investigate the performance of AI-driven automated pulp space segmentation on CBCT images.


A comprehensive electronic search was performed using PubMed, Web of Science, and Cochrane databases, up until February 2024. Two independent reviewers participated in the selection of studies, data extraction, and evaluation of the included studies. Any disagreements were resolved by a third reviewer. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was used to assess the risk of bias.


Thirteen studies that met the eligibility criteria were included. Most studies demonstrated high accuracy in their respective segmentation methods, although there was some variation across different structures (pulp chamber, root canal) and tooth types (single-rooted, multi-rooted). Automated segmentation showed slightly superior performance for segmenting the pulp chamber compared to the root canal and single-rooted teeth compared to multi-rooted ones. Furthermore, second mesiobuccal (MB2) canal segmentation also demonstrated high performance. In terms of time efficiency, the minimum time required for segmentation was 13 seconds.


AI-driven models demonstrated outstanding performance in pulp space segmentation. Nevertheless, these findings warrant careful interpretation, and their generalizability is limited due to the potential risk and low evidence level arising from inadequately detailed methodologies and inconsistent assessment techniques. In addition, there is room for further improvement, specifically for root canal segmentation and testing of AI performance in artifact-induced images.