Evaluation by dental professionals of an artificial intelligence-based application to measure alveolar bone loss.

ABSTRACT:

Background

Several commercial programs incorporate artificial intelligence in diagnosis, but very few dental professionals have been surveyed regarding its acceptability and usability. Furthermore, few have explored how these advances might be incorporated into routine practice.

Methods

Our team developed and implemented a deep learning (DL) model employing semantic segmentation neural networks and object detection networks to precisely identify alveolar bone crestal levels (ABCLs) and cemento-enamel junctions (CEJs) to measure change in alveolar crestal height (ACH). The model was trained and validated using a 550 bitewing radiograph dataset curated by an oral radiologist, setting a gold standard for ACH measurements. A twenty-question survey was created to compare the accu

SUBMITTER: Lee SW 

PROVIDER: S-EPMC11872301 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature