MASAMUNE Ken    Department   Graduate School of Medical Science, Graduate School of Medical Science    Position   Professor
論文種別	原著
言語種別	英語
査読の有無	査読あり
表題	3D evaluation model of facial aesthetics based on multi-input 3D convolution neural networks for orthognathic surgery.
掲載誌名	正式名：The international journal of medical robotics + computer assisted surgery : ISSNコード：14785951/1478596X
掲載区分	国外
巻・号・頁	20(3),pp.e2651
著者・共著者	MA Qingchuan†, KOBAYSHI Etsuko, JIN Siao, MASAMUNE Ken, SUENAGA Hideyuki
発行年月	2024/06/14
概要	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