A personalized automated system of 3D facial soft tissue landmarks annotation based on deep learning and computer vision.

ABSTRACT:

Objective

This study aims to develop a personalized automated system for annotating three-dimensional (3D) facial soft tissue landmarks utilizing deep learning and computer vision. By comparing the results of automated annotations with manual annotations, the accuracy and clinical applicability of the proposed algorithm were systematically evaluated, providing an efficient and precise analysis system for facial morphology research.

Methods

A total of 55 Chinese orthodontic patients (24 males, 31 females; mean age 23.4 ± 7.01 years) were recruited from Shandong University Stomatological Hospital, comprising 40 patients with normal facial morphology and 15 with severe craniofacial deformities (mandibular asymmetry, severe skeletal Class II and III; n = 5 each). This study cons

SUBMITTER: Wu Y 

PROVIDER: S-EPMC12765320 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature