Abstract
Purpose
Surgical guide plate can improve the accuracy of surgery, while its design process
is complex and time-consuming. This study aimed to use artificial intelligence (AI)
to design standardized mandibular angle ostectomy guide plates and reduce clinician
workload.
Methods
An intelligence algorithm was designed and trained to design guide plates, with a
safety-ensuring penalty factor added. A single-center retrospective cohort study was
conducted to test the algorithm among patients who visited our hospital between 2020
and 2021 for mandibular angle ostectomy. We included patients diagnosed with mandibular
angle hypertrophy and excluded those combined with other facial malformations. The
guide plate design method acted as the primary predictor, which was AI algorithm vs
experienced residents and the symmetry of plate-guided ostectomy was chosen as the
primary outcome. The safety, shape, location, effectiveness and design duration of
the guide plate were also recorded. The independent-samples t-test and Pearson's chi-squared
test were used and P-values < 0.05 were considered significant.
Results
Fifty patients (7 male, 43 female; 27±4 years) were included. There were significant
differences between the two groups in terms of safety (7.02 vs 5.25, p < 0.05) and
design duration (24.98 vs 1685.08, p ＜0.05). There was no significant difference in
ostectomy symmetry and shape, location, and effectiveness of the guide plates between
the two groups.
Conclusions
The intelligent algorithm can improve safety and save time for guide plate design,
ensuring other quality of the guide plates. It has good application prospects accurate
mandibular angle ostectomy.
Keywords
To read this article in full you will need to make a payment
Purchase one-time access:
Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online accessOne-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
Subscribe:
Subscribe to Journal of Plastic, Reconstructive & Aesthetic SurgeryAlready a print subscriber? Claim online access
Already an online subscriber? Sign in
Register: Create an account
Institutional Access: Sign in to ScienceDirect
References
- The Accuracy of Computer Image-Guided Template for Mandibular Angle Ostectomy.Aesthetic Plast Surg. 2015; 39: 117-123
Mao X. Research of Computer-aided Design and Clinical Application for Mandibular Osteotomy 2018 .
- Three-dimensional display of human organs from computed tomograms.Computer Graphics and Image Processing. 1979; 9: 1-21
- Three-dimensional reconstruction of craniofacial deformity using computed tomography.Neurosurgery. 1983; 13: 534-541
- Three dimensional CT reconstruction images for craniofacial surgical planning and evaluation.Radiology. 1984; 150: 179-184
- Review of three-dimensional (3D) surface imaging for oncoplastic, reconstructive and aesthetic breast surgery.Breast. 2015; 24: 331-342
- PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation.30th Ieee Conference on Computer Vision and Pattern Recognition (Cvpr 2017). 2017; : 77-85
- Automated Anatomical Landmark Detection on Distal Femur Surface Using Convolutional Neural Network.2015 Ieee 12th International Symposium on Biomedical Imaging (Isbi). 2015; : 17-21
- Using Artificial Intelligence to Measure Facial Expression following Facial Reanimation Surgery.Plast Reconstr Surg. 2020; 146: 1147-1150
- Comparison of an unsupervised machine learning algorithm and surgeon diagnosis in the clinical differentiation of metopic craniosynostosis and benign metopic ridge.Sci Rep. 2018; 8: 6312
- Role of Simulation and Artificial Intelligence in Plastic Surgery Training.Plast Reconstr Surg. 2020; 146: 390e-391ee
- A novel augmented reality system for displaying inferior alveolar nerve bundles in maxillofacial surgery.Sci Rep. 2017; 7: 42365
- Relation-Shape Convolutional Neural Network for Point Cloud Analysis.2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019; : 8887-8896
- RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds.2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020; : 11105-11114
- PointNet plus plus: Deep Hierarchical Feature Learning on Point Sets in a Metric Space.Advances in Neural Information Processing Systems 30 (Nips 2017). 2017; 30
- PCT: Point cloud transformer.Computational Visual Media. 2021; 7: 187-199
- Graph Attention Convolution for Point Cloud Semantic Segmentation.2019 Ieee/Cvf Conference on Computer Vision and Pattern Recognition (Cvpr 2019). 2019; : 10288-10297
- Dynamic Graph CNN for Learning on Point Clouds.Acm Transactions on Graphics. 2019; 38
- The position of the inferior alveolar nerve at the mandibular angle: An anatomic consideration for aesthetic mandibular angle reduction.Annals of Plastic Surgery. 2004; 53: 50-55
- Assessment of an Artificial Intelligence Mandibular Osteotomy Design System: A Retrospective Study.Aesthetic Plast Surg. 2022;
- Three-Dimensional Analysis of Mandibular Angle Classification and Aesthetic Evaluation of the Lower Face in Chinese Female Adults.Ann Plast Surg. 2018; 81: 12-17
- Three-dimensional reconstruction of the mandibular canal.Chinese Journal of Aesthetic Medicine. 2009; 18: 1277-1278
- The accuracy of a surgical template for mandibular angle osteotomy: a prospective randomized controlled trial.Chin J Aesth Plast Surg. 2018; 29: 524-26+41
Article info
Publication history
Accepted:
May 15,
2023
Received:
June 20,
2022
Publication stage
In Press Journal Pre-ProofFootnotes
1This work was supported by Shanghai Municipal Education Commission—Gaofeng Clinical Medicine Grant Support (20161420); Clinical Research Program of 9th People's Hospital affiliated to Shanghai Jiao Tong university School of Medicine [No. JYLJ202108]; Shanghai Municipal Key Clinical Specialty [No. shslczdzk00901].
Identification
Copyright
© 2023 Published by Elsevier Ltd on behalf of British Association of Plastic, Reconstructive and Aesthetic Surgeons.
