Barbosa LM,
Silva JAA, da Silva JIS, Ren TI, Vasconcelos BCE, Filho JRL. Artificial Neural Network-Assisted
Facial Analysis for Planning of Orthognathic Surgery. J Clin Exp Dent. 2024;16(11):e1386-92.
doi:10.4317/jced.62088
https://doi.org/10.4317/jced.62088
_____
References
1.
Patcas R, Bernini DAJ, Volokitin A, Agustsson E, Rothe R, Timofte R. Applying artificial intelligence
to assess the impact of orthognathic treatment on facial attractiveness and estimated age. Int J Oral e Maxillofac Surg 2019: 48:
77-83. |
|
|
|
2.
Arnett GW, Bergman RT. Facial keys
to orthodontic planning. Part I. Am J Orthod Dentofacial Orthop 1993: 103:
395-411. |
|
|
|
3.
Bouletreau P, Makaremi M,
Ibrahim B, Louvrier A, Sigaux
N, Artificial Intelligence: Applications
in Orthognathic Surgery.
J of Stomatol Oral Maxillofac
Surg 2019: 4: 347-54. |
|
|
|
4.
Kaplan A, Haenlein M. Siri,
Siri, in my hand: who's the fairest
in the land? On the interpretations,
illustrations, and implications
of artificial intelligence. Bus Horiz
2019: 62:15-25. |
|
|
|
5.
Erickson BJ, Korfiatis P, Akkus
Z, Kline TL. Machine learning
for medical imaging. Radiographics 2017: 37: 505-15. |
|
|
|
6.
Arik SO, Ibragimov B, Xing L. Fully automated quantitative cephalometry using convolutional neural networks.
J Med Imaging 2017: 4:
53-6. |
|
|
|
7.
Miki Y, Muramatsu C, Hayashi T, Zhou X, Hara T, Katsumata A. Classification of teeth in cone-beam CT using deep convolutional neural network. Comput Biol Med 2017: 1: 24-9. |
|
|
|
8.
Koestinger M, Wohlhart P,
Roth PM, Bischof H. Annotated facial landmarks in the wild: A largescale, real-world database for facial landmark localization. ICCV Workshop; Barcelona, Espanha. IEEE 2011: 2144-51. |
|
|
|
9.
Sagonas C, Tzimiropoulos
G, Zafeirious S, Pantic
M.300 faces in-the-wild challenge:
The first facial landmark localization challenge. ICCV Workshop; Sydney,
NSW, Australia. IEEE 2013: 397-403. |
|
|
|
10.
Shen J, Zafeiriou S, Chrysos GG, Kossaifi J, Tzimiropoulos G, Pantic M. The first facial landmark tracking in-the-wild challenge: Benchmark and results. ICCV Workshop; Santiago, Chile. IEEE 2015:
1003-11. |
|
|
|
11.
Liu Y, Wei F, Shao J, Sheng L, Yan J, Wang X. Exploring disentangled feature representation beyond face identification. CVPR;
2018; Salt Lake City, UT, USA. IEEE/CVF 2018: 2080-9. |
|
|
|
12.
Dong X, Yang Y. Teacher Supervises Students How to Learn From Partially
Labeled Images for Facial Landmark Detection. International Conference
on Computer Vision; Seoul, South Korea. IEEE/CVF 2019: 783-92. |
|
|
|
13.
Wu Y, Hassner T, Kim K, Medioni G, Natarajan P. Facial landmark detection with tweaked convolutional neural networks.
IEEE Trans Pattern Anal
Mach Intel 2018: 40: 3067-74. |
|
|
|
14.
Guo J, Zhu X, Yang Y,
Yang F, Lei Z, Li SZ. Towards
Fast, Accurate and Stable 3D Dense Face Alignment. ECCV; Lecture Notes
in Computer Science 2020:
13: 152-68. |
|
|
|
15.
Serengil SI, Ozpinar A. HyperExtended LightFace: A
Facial Attribute Analysis
Framework. International Conference on Engineering and Emerging Technologies (ICEET); Istanbul,
Turkey. IEEE 2021: 1-4. |
|
|
|
16.
Zhang K, Zhang Z, Li Z, Qiao Y. Joint
face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Processing Letters 2016: 23:
1499-599. |
|
|
|
17.
Deng J, Guo J, Xue N, Zafeiriou S. Arcface: Additive angular margin loss for
deep face recognition." Conference on Computer Vision
and Pattern Recognition
(CVPR); Long Beach, CA, USA. IEEE/CVF 2019: 4685-94. |
|
|
|
18.
Ren S, Cao X, Wei Y, Sun J. Face Alignment
at 3000 FPS via Regressing
Local Binary Features.
IEEE Conference on Computer Vision and Pattern Recognition; Columbus,
OH, USA. IEEE 2014: 1685-92. |
|
|
|
19.
Kingma, Diederik P., and
Jimmy Ba. Adam: A method for
stochastic optimization. arXiv preprint arXiv:1412.6980
2014. |
|
|
|
20.
Altman, DG. Practical statistics for medical research. CRC press, 1990. |
|
|
|
21.
Fourcade A, Khonsari RH.
Deep learning in medical image
analysis: a third eye for doctors.
Journal of Stomatology,
Oral and Maxillofacial Surgery
2019: 145: 279-288. |
|
|
|
22.
Lecun Y, Bengio Y, Hinton G. Deep learning. Nature 2015: 14: 27-38. |
|
|
|
23.
Data Science Academy.
Deep Learning Book, 2019. DisponÃvel
em: <http://www.deeplearningbook.com.br/>. Acesso em: 05 dezembro. 2020. |
|
|
|
24.
Ming Yan, Jixiang Guo, Weidong Tian, Zhang Yi, Symmetric convolutional neural network for mandible
segmentation, Knowledge-Based
Systems 2018: 63-71. |