Almarhoumi AA.
Accuracy of Artificial Intelligence in Predicting Facial Changes
Post-Orthognathic Surgery: A Comprehensive Scoping Review. J Clin Exp Dent.
2024;16(5):e624-33.
doi:10.4317/jced.61500
https://doi.org/10.4317/jced.61500
______
References
1.
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. |
|
|
|
2.
Benke K, Benke G. Artificial intelligence and big data in public health. Int
J Environ Res Public Health. 2018;15:2796. |
|
|
|
3.
Wang XL, Liu J, Li ZQ, Luan ZL. Application of physical examination data on
health analysis and intelligent diagnosis. Biomed Res Int. 2021;2021:8828677. |
|
|
|
4.
Choi HI, Jung SK, Baek SH, Lim WH, Ahn SJ, Yang IH, et al. Artificial
intelligent model with neural network machine learning for the diagnosis of
orthognathic surgery. J Craniofac Surg. 2019;30:1986-9. |
|
|
|
5.
Shan T, Tay FR, Gu L. Application of artificial intelligence in dentistry. J
Dent Res. 2021;100:232-44. |
|
|
|
6.
Ramesh AN, Kambhampati C, Monson JRT, Drew PJ. Artificial intelligence in
medicine. Ann R Coll Surg Engl. 2004;86:334. |
|
|
|
7.
Alom MZ, Taha TM, Yakopcic C, Westberg S, Sidike P, Nasrin MS, et al. A
state-of-the-art survey on deep learning theory and architectures.
Electronics. 2019;8:292. |
|
|
|
8.
Agatonovic-Kustrin S, Beresford R. Basic concepts of artificial neural
network (ANN) modeling and its application in pharmaceutical research. J
Pharm Biomed Anal. 2000;22:717-27. |
|
|
|
9.
Alzubaidi L, Zhang J, Humaidi AJ, Al-Dujaili A, Duan Y, Al-Shamma O, et al.
Review of deep learning: concepts, CNN architectures, challenges,
applications, future directions. J Big Data. 2021;8:53. |
|
|
|
10.
Bulatova G, Kusnoto B, Grace V, Tsay TP, Avenetti DM, Sanchez FJC. Assessment
of automatic cephalometric landmark identification using artificial
intelligence. Orthod Craniofac Res. 2021;24:37-42. |
|
|
|
11.
Kim J, Kim I, Kim Y, Kim M, Cho J, Hong M, et al. Accuracy of automated
identification of lateral cephalometric landmarks using cascade convolutional
neural networks on lateral cephalograms from nationwide multi-centres. Orthod
Craniofac Res. 2021;24:59-67. |
|
|
|
12.
Milam ME, Koo CW. The current status and future of FDA-approved artificial
intelligence tools in chest radiology in the United States. Clin Radiol.
2023;78:115-22. |
|
|
|
13.
Albalawi F, Alamoud KA. Trends and Application of Artificial Intelligence
Technology in Orthodontic Diagnosis and Treatment Planning-A Review. Appl Sci.
2022;12. |
|
|
|
14.
Kazimierczak N, Kazimierczak W, Serafin Z, Nowicki P, Nożewski J,
Janiszewska-Olszowska J. AI in Orthodontics: Revolutionizing Diagnostics and
Treatment Planning. J Clin Med. 2024;13:344. PMid:38256478
PMCid:PMC10816993 |
|
|
|
15.
Allareddy V, Rengasamy Venugopalan S, Nalliah RP, Caplin JL, Lee MK,
Allareddy V. Orthodontics in the era of big data analytics. Orthod
Craniofacial Res. 2019;22:8-13. |
|
|
|
16.
Khanagar SB, Al-Ehaideb A, Vishwanathaiah S, Maganur PC, Patil S, Naik S, et
al. Scope and performance of artificial intelligence technology in
orthodontic diagnosis, treatment planning, and clinical decision-making - A
systematic review. J Dent Sci. 2021;16:482-92. |
|
|
|
17.
Moon JH, Kim MG, Hwang HW, Cho SJ, Donatelli RE, Lee SJ. Evaluation of an
individualized facial growth prediction model based on the multivariate
partial least squares method. Angle Orthod. 2022;92:705-13. |
|
|
|
18.
Wen YF, Wong HM, McGrath CP. Developmental shape changes in facial
morphology: Geometric morphometric analyses based on a prospective,
population-based, Chinese cohort in Hong Kong. PLoS One. 2019;14:e0218542. |
|
|
|
19.
Bral A, Olate S, Zaror C, Mensink G, Coscia G, Mommaerts MY. A prospective
study of soft-and hard-tissue changes after mandibular advancement surgery:
midline changes in the chin area. Am J Orthod Dentofac Orthop.
2020;157:662-7. |
|
|
|
20.
Lim YN, Yang BE, Byun SH, Yi SM, On SW, Park IY. Three-dimensional digital
image analysis of skeletal and soft tissue points A and B after orthodontic
treatment with premolar extraction in bimaxillary protrusive patients.
Biology (Basel). 2022;11:381. |
|
|
|
21.
McGrath TA, Alabousi M, Skidmore B, Korevaar DA, Bossuyt PMM, Moher D, et al.
Recommendations for reporting of systematic reviews and meta-analyses of
diagnostic test accuracy: a systematic review. Syst Rev. 2017;6:194. |
|
|
|
22.
Lu CH, Ko EWC, Liu L. Improving the video imaging prediction of postsurgical
facial profiles with an artificial neural network. J Dent Sci 2009;4:118-29. |
|
|
|
23.
Ma L, Xiao D, Kim D, Lian C, Kuang T, Liu Q, et al. Simulation of
Postoperative Facial Appearances via Geometric Deep Learning for Efficient
Orthognathic Surgical Planning. IEEE Trans Med Imaging. 2023;42:336-45. |
|
|
|
24.
ter Horst R, van Weert H, Loonen T, Bergé S, Vinayahalingam S, Baan F, et al.
Three-dimensional virtual planning in mandibular advancement surgery: Soft
tissue prediction based on deep learning. J Cranio-Maxillofacial Surg.
2021;49:775-82. |
|
|
|
25.
Knoops PGM, Papaioannou A, Borghi A, Breakey RWF, Wilson AT, Jeelani O, et
al. A machine learning framework for automated diagnosis and computer-assisted
planning in plastic and reconstructive surgery. Sci Rep. 2019;9:1-12. |
|
|
|
26.
Tanikawa C, Yamashiro T. Development of novel artificial intelligence systems
to predict facial morphology after orthognathic surgery and orthodontic
treatment in Japanese patients. Sci Rep. 2021;11:15853. |
|
|
|
27.
Ali R, Lei R, Shi H, Xu J. Cranio-maxillofacial post-operative face
prediction by deep spatial multiband VGG-NET CNN. Am J Transl Res.
2022;14:2527-39. PMid:35559377
PMCid:PMC9091107 |
|
|
|
28.
Lampen N, Kim D, Fang X, Xu X, Kuang T, Deng HH, et al. Deep learning for
biomechanical modeling of facial tissue deformation in orthognathic surgical
planning. Int J Comput Assist Radiol Surg. 2022;17:945-52. |
|
|
|
29.
Ma L, Kim D, Lian C, Xiao D, Kuang T, Liu Q, et al. Deep Simulation of Facial
Appearance Changes Following Craniomaxillofacial Bony Movements in
Orthognathic Surgical Planning. Lect Notes Comput Sci (Including Subser Lect
Notes Artif Intell Lect Notes Bioinformatics). 2021;12904 LNCS:459-68. |
|
|
|
30.
Yuan P, Mai H, Li J, Ho DC-Y, Lai Y, Liu S, et al. Design, development and
clinical validation of computer-aided surgical simulation system for
streamlined orthognathic surgical planning. Int J Comput Assist Radiol Surg.
2017;12:2129-43. |
|
|
|
31.
Ullah R, Turner PJ, Khambay BS. Accuracy of three-dimensional soft tissue
predictions in orthognathic surgery after Le Fort I advancement osteotomies.
Br J Oral Maxillofac Surg. 2015;53:153-7. |
|
|
|
32.
Kim D, Kuang T, Rodrigues YL, Gateno J, Shen SGF, Wang X, et al. A new
approach of predicting facial changes following orthognathic surgery using
realistic lip sliding effect. Med Image Comput Comput Assist Interv. 2019:11768:336-344. |
|
|
|
33.
Tonutti M, Gras G, Yang GZ. A machine learning approach for real-time
modelling of tissue deformation in image-guided neurosurgery. Artif Intell
Med. 2017;80:39-47. |
|
|
|
34.
Ryu JY, Chung HY, Choi KY. Potential role of artificial intelligence in
craniofacial surgery. Arch Craniofacial Surg. 2021;22:223. |
|
|
|
35.
Cheng M, Zhang X, Wang J, Yang Y, Li M, Zhao H, et al. Prediction of
orthognathic surgery plan from 3D cephalometric analysis via deep learning.
BMC Oral Health. 2023;23:1-11. |
|
|
|
36.
Schwendicke F, Singh T, Lee JH, Gaudin R, Chaurasia A, Wiegand T, et al.
Artificial intelligence in dental research: Checklist for authors, reviewers,
readers. J Dent. 2021;107:103610. |
|
|
|
37.
Hashimoto DA, Rosman G, Rus D, Meireles OR. Artificial intelligence in
surgery: promises and perils. Ann Surg. 2018;268:70. |
|
|
|
38.
Abdoullaev A. Artificial intelligence vs machine learning vs artificial
neural networks vs deep learning 2021.
https://www.bbntimes.com/science/artificial-intelligence-vs-machine-learning-vs-artificial-neural-networks-vs-deep-learning
(accessed February 23, 2024). |
|
|
|
39.
Singh A. Artificial Neural Networks 2018.
https://msatechnosoft.in/blog/artificial-neural-network-types-feed-forward-feedback-structure-perceptron-machine-learning-applications/
(accessed February 23, 2024). |
|
|
|
40.
Deshpande A. A Beginner's Guide To Understanding Convolutional Neural
Networks 2016. https://adeshpande3.github.io/A-Beginner's-Guide-To-Understanding-Convolutional-Neural-Networks/
(accessed February 23, 2024). |
|
|