Application Progress of Artificial Intelligence in Orthodontic Diagnosis, Treatment, and Soft Tissue Prediction

doi:10.13701/j.cnki.kqyxyj.2023.10.002

Abstract

Abstract: In recent years, the application of artificial intelligence in the field of orthodontics is increasing, and many artificial intelligence models are applied to orthodontic diagnosis, treatment planning, prognosis prediction, etc. The research shows that the application of artificial intelligence in orthodontics can improve the efficiency of diagnosis and treatment with high accuracy, but there are also problems such as insufficient data, single sample source, poor interpretability of artificial intelligence model, etc. This review intends to provide an overview of the effectiveness of artificial intelligence models used in orthodontic diagnosis, treatment planning, and facial profile prediction after orthodontic treatment or orthognathic surgery, so as to comprehensively understand the status of artificial intelligence in orthodontics and provide reference for relevant research and clinical application.

Key words: artificial intelligence, machine learning, orthodontic, soft tissue prediction

LIU Jiayi, ZHOU Yanheng. Application Progress of Artificial Intelligence in Orthodontic Diagnosis, Treatment, and Soft Tissue Prediction[J]. Journal of Oral Science Research, 2023, 39(10): 862-865.

References

[1] Deo RCC. Machine learning in medicine [J]. Circulation, 2015, 132(20): 1920-1930.
[2] Park YS, Choi JH, Kim Y, et al. Deep learning-based prediction of the 3D postorthodontic facial changes [J]. J Dent Res, 2022, 101(11): 1372-1379.
[3] Jeong SH, Woo MW, Shin DS, et al. Three-dimensional postoperative results prediction for orthognathic surgery through deep learning-based alignment network [J]. J Pers Med, 2022, 12(6): 998.
[4] Tanikawa C, Yamashiro T. Development of novel artificial intelligence systems to predict facial morphology after orthognathic surgery and orthodontic treatment in Japanese patients [J]. Sci Rep, 2021, 11(1): 15853.
[5] Zhang X, Mei L, Yan X, et al. Accuracy of computer-aided prediction in soft tissue changes after orthodontic treatment [J]. Am J Orthod Dentofacial Orthop, 2019, 156(6): 823-831.
[6] Moran M, Faria M, Giraldi G, et al. Classification of approximal caries in bitewing radiographs using convolutional neural networks [J]. Sensors (Basel), 2021, 21(15): 5192.
[7] Lee JH, Kim DH, Jeong SN. Diagnosis of cystic lesions using panoramic and cone beam computed tomographic images based on deep learning neural network [J]. Oral Dis, 2020, 26(1): 152-158.
[8] Lee CT, Kabir T, Nelson J, et al. Use of the deep learning approach to measure alveolar bone level [J]. J Clin Periodontol, 2022, 49(3): 260-269.
[9] Ari T, Sağlam H,Öksüzoğlu H, et al. Automatic feature segmentation in dental periapical radiographs [J]. Diagnostics (Basel), 2022, 12(12): 3081.
[10] Yu HJ, Cho SR, Kim MJ, et al. Automated skeletal classification with lateral cephalometry based on artificial intelligence [J]. J Dent Res, 2020, 99(3): 249-256.
[11] Song Y, Qiao X, Iwamoto Y, et al. Automatic cephalometric landmark detection on X-ray images using a deep-learning method [J]. Appl Sci-Basel, 2020, 10(7): 16.
[12] Qian J, Cheng M, Tao Y,et al. CephaNet: An improved faster R-CNN for cephalometric landmark detection[C]. 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), 2019: 868-871.
[13] Jiang F, Guo Y, Yang C, et al. Artificial intelligence system for automated landmark localization and analysis of cephalometry [J]. Dentomaxillofac Radiol, 2023, 52(1): 20220081.
[14] Chen X, Lian C, Deng HH, et al. Fast and accurate craniomaxillofacial landmark detection via 3D faster R-CNN [J]. IEEE Trans Med Imaging, 2021, 40(12): 3867-3878.
[15] Dot G, Schouman T, Chang S, et al. Automatic 3-dimensional cephalometric landmarking via deep learning [J]. J Dent Res, 2022, 101(11): 1380-1387.
[16] 成都牙讯科技有限公司. Uceph [EB/OL]. [2023-02-24]. http://www.uceph.com/index.html.
[17] 成都玻尔兹曼智贝科技有限公司.智贝云影[EB/OL]. [2023-02-24]. http://www.aortho360.com/.
[18] Nestman TS, Marshall SD, Qian F, et al. Cervical vertebrae maturation method morphologic criteria: poor reproducibility [J]. Am J Orthod Dentofacial Orthop, 2011, 140(2): 182-188.
[19] Amasya H, Yildirim D, Aydogan T, et al. Cervical vertebral maturation assessment on lateral cephalometric radiographs using artificial intelligence: comparison of machine learning classifier models [J]. Dentomaxillofac Radiol, 2020, 49(5): 20190441.
[20] Atici SF, Ansari R, Allareddy V, et al. Fully automated determination of the cervical vertebrae maturation stages using deep learning with directional filters [J]. PLoS One, 2022, 17(7): e0269198.
[21] Seo H, Hwang J, Jeong T, et al. Comparison of deep learning models for cervical vertebral maturation stage classification on lateral cephalometric radiographs [J]. J Clin Med, 2021, 10(16): 3591.
[22] Cui Z, Li C, Wang W. ToothNet: Automatic tooth instance segmentation and identification from cone beam CT images[C]. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019: 6361-6370.
[23] Xu J, Liu J, Zhang D, et al. A 3D segmentation network of mandible from CT scan with combination of multiple convolutional modules and edge supervision in mandibular reconstruction [J]. Comput Biol Med, 2021, 138: 104925.
[24] Wallner J, Mischak I, Jan Egger. Computed tomography data collection of the complete human mandible and valid clinical ground truth models [J]. Sci Data, 2019, 6: 190003.
[25] Cui Z, Fang Y, Mei L, et al. A fully automatic AI system for tooth and alveolar bone segmentation from cone-beam CT images [J]. Nat Commun, 2022, 13(1): 2096.
[26] Li P, Kong D, Tang T, et al. Orthodontic treatment planning based on artificial neural networks [J]. Sci Rep, 2019, 9(1): 2037.
[27] Suhail Y, Upadhyay M, Chhibber A, et al. Machine learning for the diagnosis of orthodontic extractions: A computational analysis using ensemble learning [J]. Bioengineering (Basel), 2020, 7(2): 55.
[28] Shimizu Y, Tanikawa C, Kajiwara T, et al. The validation of orthodontic artificial intelligence systems that perform orthodontic diagnoses and treatment planning [J]. Eur J Orthod, 2022, 44(4): 436-444.
[29] El-Dawlatly MM, Abdelmaksoud AR, Amer OM, et al. Evaluation of the efficiency of computerized algorithms to formulate a decision support system for deepbite treatment planning [J]. Am J Orthod Dentofacial Orthop, 2021, 159(4): 512-521.
[30] Kim YH, Park JB, Chang MS, et al. Influence of the depth of the convolutional neural networks on an artificial intelligence model for diagnosis of orthognathic surgery [J]. J Pers Med, 2021, 11(5): 356.
[31] Shin W, Yeom HG, Lee GH, et al. Deep learning based prediction of necessity for orthognathic surgery of skeletal malocclusion using cephalogram in Korean individuals [J]. BMC Oral Health, 2021, 21(1): 130.
[32] Jeong SH, Yun JP, Yeom HG, et al. Deep learning based discrimination of soft tissue profiles requiring orthognathic surgery by facial photographs [J]. Sci Rep, 2020, 10(1): 16235.
[33] Knoops PGM, Papaioannou A, Borghi A, et al. A machine learning framework for automated diagnosis and computer-assisted planning in plastic and reconstructive surgery [J]. Sci Rep, 2019, 9(1): 13597.
[34] Lin HH, Chiang WC, Yang CT, et al. On construction of transfer learning for facial symmetry assessment before and after orthognathic surgery [J]. Comput Methods Programs Biomed, 2021, 200: 105928.
[35] Thurzo A, Kurilová V, Varga I. Artificial intelligence in orthodontic smart application for treatment coaching and its impact on clinical performance of patients monitored with AI-telehealth system [J]. Healthcare (Basel), 2021, 9(12): 1695.
[36] Li S, Guo Z, Lin J, et al. Artificial intelligence for classifying and archiving orthodontic images [J]. Biomed Res Int, 2022, 2022: 1473977.
[37] Tao T, Zou K, Jiang R, et al. Artificial intelligence-assisted determination of available sites for palatal orthodontic mini implants based on palatal thickness through CBCT [J]. Orthod Craniofac Res, 2023, 26(3): 491-499.
[38] Santosh KC, Ghosh S. Covid-19 imaging tools: How big data is big? [J]. J Med Syst, 2021, 45(7): 71.
[39] Rudin C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead [J]. Nat Mach Intell, 2019, 1(5): 206-215.