[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. |