| [1] |
Farhad B. Naini. 正颌外科学原则、策略和实践[M]. 上海: 上海科学技术出版社, 2021:390-393.
|
| [2] |
McNamara L, McNamara JA Jr, Ackerman MB, et al. Hard- and soft-tissue contributions to the esthetics of the posed smile in growing patients seeking orthodontic treatment[J]. Am J Orthod Dentofac Orthop, 2008, 133(4):491-499.
doi: 10.1016/j.ajodo.2006.05.042
|
| [3] |
Swennen GRJ. 3D数字化正颌外科设计与治疗[M]. 西安: 世界图书出版公司, 2018:217-220.
|
| [4] |
Shaheen E, Sun Y, Jacobs R, et al. Three-dimensional printed final occlusal splint for orthognathic surgery: Design and validation[J]. Int J Oral Maxillofac Surg, 2017, 46(1):67-71.
|
| [5] |
Erickson BJ. Basic artificial intelligence techniques: Machine learning and deep learning[J]. Radiol Clin North Am, 2021, 59(6):933-940.
doi: 10.1016/j.rcl.2021.06.004
|
| [6] |
Hang JY, Zhang ML. Collaborative learning of label semantics and deep label-specific features for multi-label classification[J]. IEEE Trans Pattern Anal Mach Intell, 2021, PP: 2021Dec20.
|
| [7] |
Park SH, Han K. Methodologicguide for evaluating clinical performance and effect of artificial intelligence technology for medical diagnosis and prediction[J]. Radiology, 2018, 286(3):800-809.
doi: 10.1148/radiol.2017171920
|
| [8] |
London AJ. Artificial intelligence and black-box medical decisions: Accuracy versus explainability[J]. Hastings Cent Rep, 2019, 49(1):15-21.
doi: 10.1002/hast.973
pmid: 30790315
|
| [9] |
Jin L, Shi F, Chun QP, et al. Artificial intelligence neuropathologist for glioma classification using deep learning on hematoxylin and eosin stained slide images and molecular markers[J]. Neuro-oncology, 2021, 23(1):44-52.
doi: 10.1093/neuonc/noaa163
pmid: 32663285
|
| [10] |
王思维, 叶嘉慧, 刘云松, 等. 数字化虚拟仿真设计在口腔美学修复中的应用和研究进展[J]. 中华口腔医学杂志, 2022, 57(1):101-106.
|
| [11] |
Bulatova G, Kusnoto B, Grace V, et al. Assessment of automatic cephalometric landmark identification using artificial intelligence[J]. Orthod Craniofac Res, 2021, 24(Suppl 2):37-42.
doi: 10.1111/ocr.v24.s2
|
| [12] |
Wang CW, Huang CT, Lee JH, et al. A benchmark for comparison of dental radiography analysis algorithms[J]. Med Image Anal, 2016, 31: 63-76.
doi: 10.1016/j.media.2016.02.004
|
| [13] |
Yao J, Zeng W, He T, et al. Automatic localization of cephalometric landmarks based on convolutional neural network[J]. Am J Orthod Dentofacial Orthop, 2022, 161(3):e250-e259.
doi: 10.1016/j.ajodo.2021.09.012
|
| [14] |
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.
doi: 10.1177/0022034520901715
pmid: 31977286
|
| [15] |
Elnagar MH, Aronovich S, Kusnoto B. Digital workflow for combined orthodontics and orthognathic surgery[J]. Oral Maxillofac Surg Clin North Am, 2020, 32(1):1-14.
doi: 10.1016/j.coms.2019.08.004
|
| [16] |
Xiao DQ, Lian CF, Deng H, et al. Estimating reference bony shape models for orthognathic surgical planning using 3D point-cloud deep learning[J]. IEEE J Biomed Health Inform, 2021, 25(8):2958-2966.
doi: 10.1109/JBHI.2021.3054494
|
| [17] |
Fan Y, Beare R, Matthews H, et al. Marker-based watershed transform method for fully automatic mandibular segmentation from CBCT images[J]. Dentomaxillofac Radiol, 2019, 48(2):20180261.
doi: 10.1259/dmfr.20180261
|
| [18] |
Ajmera DH, Hsung RTC, Singh P, et al. Three-dimensional assessment of facial asymmetry in Class Ⅲ subjects. Part 1: A retrospective study evaluating postsurgical outcomes[J]. Clin Oral Investig, 2022, 26(7):4947-4966.
doi: 10.1007/s00784-022-04463-4
|
| [19] |
Zheng X, Wang L, Zhang B, et al. Accuracy of two midsagittal Planes in three-dimensional analysis and their measurement in patients with skeletal mandibular deviation: A comparative study[J]. Br J Oral Maxillofac Surg, 2018, 56(7):600-606.
doi: S0266-4356(18)30202-X
pmid: 29980353
|
| [20] |
Payer C, Štern D, Bischof H, et al. Integrating spatial configuration into heatmap regression based CNNs for landmark localization[J]. Med Image Anal, 2019, 54: 207-219.
doi: S1361-8415(18)30578-4
pmid: 30947144
|
| [21] |
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.
doi: 10.1177/00220345221112333
|
| [22] |
罗恩. 人工智能正颌外科的探索与临床初步应用[J]. 国际口腔医学杂志, 2022, 49(2):125-131.
|
| [23] |
Weiss RO 2nd, Ong AA, Reddy LV, et al. Orthognathic surgery-LeFort I osteotomy[J]. Facial Plast Surg, 2021, 37(6):703-708.
doi: 10.1055/s-0041-1735308
pmid: 34530468
|
| [24] |
Qiu XH, Han WQ, Dai L, et al. Assessment of an artificial intelligence mandibular osteotomy design system: A retrospective study[J]. Aesthetic Plast Surg, 2022, 46(3):1303-1313.
doi: 10.1007/s00266-021-02698-2
|
| [25] |
Chung M, Lee JY, Song W, et al. Automatic registration between dental cone-beam CT and scanned surface via deep pose regression neural networks and clustered similarities[J]. IEEE Trans Med Imaging, 2020, 39(12):3900-3909.
doi: 10.1109/TMI.42
|
| [26] |
万竹青, 沈国芳. 正颌手术上颌骨精确定位技术的进展[J]. 口腔医学, 2018, 38(10):934-937.
|
| [27] |
Badai J, Bu Q, Zhang L. Review of artificial intelligence applications and algorithms for brain organoid research[J]. Interdiscip Sci, 2020, 12(4):383-394.
doi: 10.1007/s12539-020-00386-4
|
| [28] |
Suarez-Ahedo C, Gui CC, Martin TJ, et al. Robotic-arm assisted total hip arthroplasty results in smaller acetabular cup size in relation to the femoral head size: A matched-pair controlled study[J]. Hip Int, 2017, 27(2):147-152.
doi: 10.5301/hipint.5000418
pmid: 28362049
|
| [29] |
Lin L, Xu C, Shi YY, et al. Preliminary clinical experience of robot-assisted surgery in treatment with genioplasty[J]. Sci Rep, 2021, 11(1):6365.
doi: 10.1038/s41598-021-85889-w
pmid: 33739026
|
| [30] |
Sun MZ, Chai YH, Chai G, et al. Fully automatic robot-assisted surgery for mandibular angle split osteotomy[J]. J Craniofac Surg, 2020, 31(2):336-339.
doi: 10.1097/SCS.0000000000005587
pmid: 31232983
|
| [31] |
Elshebiny T, Morcos S, Mohammad A, et al. Accuracy of three-dimensional soft tissue prediction in orthognathic cases using dolphin three-dimensional software[J]. J Craniofac Surg, 2019, 30(2):525-528.
doi: 10.1097/SCS.0000000000005037
pmid: 30358749
|
| [32] |
Resnick CM, Dang RR, Glick SJ, et al. Accuracy of three-dimensional soft tissue prediction for Le Fort I osteotomy using Dolphin 3D software: A pilot study[J]. Int J Oral Maxillofac Surg, 2017, 46(3):289-295.
|
| [33] |
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.
doi: 10.1038/s41598-021-95002-w
pmid: 34349151
|
| [34] |
胡婷姿, 杨海萍, 肖沛, 等. Zebris下颌运动分析系统的应用与研究现状[J]. 口腔医学, 2021, 41(12):1129-1133.
|
| [35] |
Venkata HS, Alsadoon A, Prasad PWC, et al. A novel mixed reality in breast and constructive jaw surgical tele-presence[J]. Comput Methods Programs Biomed, 2019, 177: 253-268.
doi: 10.1016/j.cmpb.2019.05.025
|
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