Machine learning for automated identification of anatomical landmarks in ultrasound periodontal imaging.

ABSTRACT:

Objectives

To identify landmarks in ultrasound periodontal images and automate the image-based measurements of gingival recession (iGR), gingival height (iGH), and alveolar bone level (iABL) using machine learning.

Methods

We imaged 184 teeth from 29 human subjects. The dataset included 1580 frames for training and validating the U-Net convolutional neural network machine learning model, and 250 frames from new teeth that were not used in training for testing the generalization performance. The predicted landmarks, including the tooth, gingiva, bone, gingival margin (GM), cementoenamel junction (CEJ), and alveolar bone crest (ABC), were compared to manual annotations. We further demonstrated automated measurements of the clinical metrics iGR, iGH, and iABL.

Results

Ov

SUBMITTER: Qi B 

PROVIDER: S-EPMC11879227 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature