%0 Journal Article
%T Automatic soft-tissue analysis on orthodontic frontal and lateral facial photographs based on deep learning.
%A Chang Q
%A Bai Y
%A Wang S
%A Wang F
%A Wang Y
%A Zuo F
%A Xie X
%J Orthod Craniofac Res
%V 0
%N 0
%D 2024 Jul 5
%M 38967085
%F 2.563
%R 10.1111/ocr.12830
%X <B>BACKGROUND: </B>To establish the automatic soft-tissue analysis model based on deep learning that performs landmark detection and measurement calculations on orthodontic facial photographs to achieve a more comprehensive quantitative evaluation of soft tissues.<BR><B>METHODS: </B>A total of 578 frontal photographs and 450 lateral photographs of orthodontic patients were collected to construct datasets. All images were manually annotated by two orthodontists with 43 frontal-image landmarks and 17 lateral-image landmarks. Automatic landmark detection models were established, which consisted of a high-resolution network, a feature fusion module based on depthwise separable convolution, and a prediction model based on pixel shuffle. Ten measurements for frontal images and eight measurements for lateral images were defined. Test sets were used to evaluate the model performance, respectively. The mean radial error of landmarks and measurement error were calculated and statistically analysed to evaluate their reliability.<BR><B>RESULTS: </B>The mean radial error was 14.44 ± 17.20 pixels for the landmarks in the frontal images and 13.48 ± 17.12 pixels for the landmarks in the lateral images. There was no statistically significant difference between the model prediction and manual annotation measurements except for the mid facial-lower facial height index. A total of 14 measurements had a high consistency.<BR><B>CONCLUSIONS: </B>Based on deep learning, we established automatic soft-tissue analysis models for orthodontic facial photographs that can automatically detect 43 frontal-image landmarks and 17 lateral-image landmarks while performing comprehensive soft-tissue measurements. The models can assist orthodontists in efficient and accurate quantitative soft-tissue evaluation for clinical application.