MASAMUNE Ken
   Department   Graduate School of Medical Science, Graduate School of Medical Science
   Position   Professor
Article types Original article
Language English
Peer review Peer reviewed
Title 3D evaluation model of facial aesthetics based on multi-input 3D convolution neural networks for orthognathic surgery.
Journal Formal name:The international journal of medical robotics + computer assisted surgery :
ISSN code:14785951/1478596X
Domestic / ForeginForegin
Volume, Issue, Page 20(3),pp.e2651
Author and coauthor MA Qingchuan†, KOBAYSHI Etsuko, JIN Siao, MASAMUNE Ken, SUENAGA Hideyuki
Publication date 2024/06/14
Summary BACKGROUND: Quantitative evaluation of facial aesthetics is an important but also time-consuming procedure in orthognathic surgery, while existing 2D beauty-scoring models are mainly used for entertainment with less clinical impact.

METHODS: A deep-learning-based 3D evaluation model DeepBeauty3D was designed and trained using 133 patients' CT images. The customised image preprocessing module extracted the skeleton, soft tissue, and personal physical information from raw DICOM data, and the predicting network module employed 3-input-2-output convolution neural networks (CNN) to receive the aforementioned data and output aesthetic scores automatically.

RESULTS: Experiment results showed that this model predicted the skeleton and soft tissue score with 0.231 ± 0.218 (4.62%) and 0.100 ± 0.344 (2.00%) accuracy in 11.203 ± 2.824 s from raw CT images.

CONCLUSION: This study provided an end-to-end solution using real clinical data based on 3D CNN to quantitatively evaluate facial aesthetics by considering three anatomical factors simultaneously, showing promising potential in reducing workload and bridging the surgeon-patient aesthetics perspective gap.
DOI 10.1002/rcs.2651