TY - JOUR
T1 - Quantification of facial symmetry in orthognathic surgery
T2 - A novel approach integrating 3D contour maps and hyper-dimensional computing
AU - Ho, Cheng Ting
AU - Lo, Lun Jou
AU - Chiang, Wen Chung
AU - Liu, Chuan Ming
AU - Lin, Hsiu Hsia
N1 - Publisher Copyright:
© 2024 Elsevier Ltd
PY - 2024/12
Y1 - 2024/12
N2 - This study aimed to enhance the evaluation of facial symmetry crucial for planning and assessing outcomes of orthognathic surgery (OGS). An innovative approach combining three-dimensional (3D) facial contour lines with hyperdimensional (HD) computing was developed for this purpose. Data were collected using 3D cone beam computed tomography (CBCT) at Chang Gung Memorial Hospital from 2016 to 2021. A comprehensive dataset was compiled, including images from 150 normal individuals and 2500 patients, totaling 5150 preoperative and postoperative facial images. A machine learning model was trained to analyze these images, and 3D contour data were used to create a facial symmetry quantification system with HD computing. Additionally, 3D CBCT data from 200 patients before and after OGS were retrospectively reviewed for clinical application. The developed facial symmetry algorithm demonstrated an overall accuracy of 84.1 %. Postoperative facial symmetry scores improved significantly, with a mean score increase of 53 %, from 2.40 to 3.63. The study culminated in the creation of a web-based system that leverages HD computing and 3D contour mapping to automate facial symmetry assessment. This system offers a user-friendly interface for rapid and accurate evaluations, facilitating better communication between clinicians and patients.
AB - This study aimed to enhance the evaluation of facial symmetry crucial for planning and assessing outcomes of orthognathic surgery (OGS). An innovative approach combining three-dimensional (3D) facial contour lines with hyperdimensional (HD) computing was developed for this purpose. Data were collected using 3D cone beam computed tomography (CBCT) at Chang Gung Memorial Hospital from 2016 to 2021. A comprehensive dataset was compiled, including images from 150 normal individuals and 2500 patients, totaling 5150 preoperative and postoperative facial images. A machine learning model was trained to analyze these images, and 3D contour data were used to create a facial symmetry quantification system with HD computing. Additionally, 3D CBCT data from 200 patients before and after OGS were retrospectively reviewed for clinical application. The developed facial symmetry algorithm demonstrated an overall accuracy of 84.1 %. Postoperative facial symmetry scores improved significantly, with a mean score increase of 53 %, from 2.40 to 3.63. The study culminated in the creation of a web-based system that leverages HD computing and 3D contour mapping to automate facial symmetry assessment. This system offers a user-friendly interface for rapid and accurate evaluations, facilitating better communication between clinicians and patients.
UR - http://www.scopus.com/inward/record.url?scp=85205591315&partnerID=8YFLogxK
U2 - 10.1016/j.compbiomed.2024.109189
DO - 10.1016/j.compbiomed.2024.109189
M3 - 文章
AN - SCOPUS:85205591315
SN - 0010-4825
VL - 183
JO - Computers in Biology and Medicine
JF - Computers in Biology and Medicine
M1 - 109189
ER -