A deep learning approach to predict the results of root coverage procedures based on intraoral photographs: a retrospective study.

ABSTRACT:

Background

Gingival recession (GR) occurs frequently in patients with periodontitis. The prediction of post-operative gingival margin (GM) holds significant importance for the development of surgical plans. The accuracy of the prediction of the root coverage procedures can be influenced by the periodontists' subjective bias and the complex anatomical factors of the affected teeth. Deep learning network may improve the accuracy and efficiency of the prediction of the root coverage procedures. This study aims to establish a deep learning model based on intraoral photographs, which could accurately predict the results of root coverage procedures.

Methods

A total of 963 teeth (408 intraoral photographs) were included to construct the model. A deep learning model based on YOLO-v8

SUBMITTER: Sun W 

PROVIDER: S-EPMC12699937 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature