| [1] |
Schwendicke F， Krois J. Precision dentistry-what it is，where it fails （yet），and how to get there [J]. Clin Oral Investig， 2022， 26（4）：3395-403. DOI： 10.1007/s00784-022-04420-1.
|
| [2] |
Mun SB， Yoo SR， Kim YJ，et al. AI-driven prediction of dental implant numbers to be placed for patient-specific treatment planning [J]. Int Dent J， 2025， 75（6）：103896. DOI： 10.1016/j.identj.2025.103896.
|
| [3] |
Kurt Bayrakdar S， Orhan K， Bayrakdar IS，et al. A deep learning approach for dental implant planning in cone-beam computed tomography images [J]. BMC Med Imaging， 2021， 21（1）：86. DOI： 10.1186/s12880-021-00618-z.
|
| [4] |
Esteva A， Robicquet A， Ramsundar B，et al. A guide to deep learning in healthcare [J]. Nat Med， 2019， 25（1）：24-29. DOI： 10.1038/s41591-018-0316-z.
|
| [5] |
Lawand G， Gonzaga L， Issa J，et al. Artificial intelligence segmentation errors in implant planning software programs：An overview[J]. Clin Implant Dent Relat Res， 2025， 27（5）：e70095. DOI： 10.1111/cid.70095.
|
| [6] |
Alshenaiber R， Cowan C， Barclay C，et al. Analysis of residual ridge morphology in a group of edentulous patients seeking NHS dental implant provision：A retrospective observational lateral cephalometric study [J]. Diagnostics （Basel）， 2021， 11（12）：2348. DOI： 10.3390/diagnostics11122348.
|
| [7] |
Cawood JI， Howell RA. A classification of the edentulous jaws [J]. Int J Oral Maxillofac Surg， 1988， 17（4）：232-236. DOI： 10.1016/s0901-5027(88)80047-x.
|
| [8] |
Palomino-Zorrilla JJ， Córdova-Limaylla NE， Rosas-Díaz JC，et al. Jawbone quality classification in dental implant planning and placement studies. A scoping review[J]. J Int Soc Prev Community Dent， 2024， 14（2）：89-97. DOI： 10.4103/jispcd.JISPCD_4_22.
|
| [9] |
Troiano G， Rapani A， Fanelli F，et al. Inter and intra-operator reliability of Lekholm and Zarb classification and proposal of a novel radiomic data-driven clustering for qualitative assessment of edentulous alveolar ridges [J]. Clin Oral Implants Res， 2024， 35（7）：729-738. DOI： 10.1111/clr.14271.
|
| [10] |
Goiato MC， dos Santos DM， Santiago JF Jr，et al. Longevity of dental implants in type Ⅳ bone：A systematic review [J]. Int J Oral Maxillofac Surg， 2014， 43（9）：1108-1116. DOI： 10.1016/j.ijom.2014.02.016.
|
| [11] |
Carneiro ALE， Reis INR， Bitencourt FV，et al. Accuracy of linear measurements for implant planning based on low-dose cone beam CT protocols：A systematic review and Meta-analysis [J]. Dentomaxillofac Radiol， 2024， 53（4）：207-221. DOI： 10.1093/dmfr/twae007.
|
| [12] |
Macrì M， D'Albis V， D'Albis G，et al. The role and applications of artificial intelligence in dental implant planning：A systematic review [J]. Bioengineering （Basel）， 2024， 11（8）：778. DOI： 10.3390/bioengineering11080778.
|
| [13] |
Gong Z， Li X， Shi M，et al. Measuring the binary thickness of buccal bone of anterior maxilla in low-resolution cone-beam computed tomography via a bilinear convolutional neural network [J]. Quant Imaging Med Surg， 2023， 13（12）：8053-8066. DOI： 10.21037/qims-23-744.
|
| [14] |
Lin Y， Shi M， Xiang D，et al. Construction of an end-to-end regression neural network for the determination of a quantitative index sagittal root inclination [J]. J Periodontol， 2022， 93（12）：1951-1960. DOI： 10.1002/jper.21-0492.
|
| [15] |
Kim YH， Shin JY， Lee A，et al. Automated cortical thickness measurement of the mandibular condyle head on CBCT images using a deep learning method [J]. Sci Rep， 2021， 11（1）：14852. DOI： 10.1038/s41598-021-94362-7.
|
| [16] |
Kim SH， Kim J， Yang S，et al. Automatic and quantitative measurement of alveolar bone level in OCT images using deep learning [J]. Biomed Opt Express， 2022， 13（10）：5468-5482. DOI： 10.1364/boe.468212.
|
| [17] |
Liu H， Duan J， Zeng P，et al. Intelligently quantifying the entire irregular dental structure [J]. J Dent Res， 2024， 103（4）：378-387. DOI： 10.1177/00220345241226871.
|
| [18] |
|
| [19] |
Ayzenberg V， Sener SB， Novick K，et al. Fast and robust visual object recognition in young children [J]. Sci Adv， 2025， 11（27）：eads6821. DOI： 10.1126/sciadv.ads6821.
|
| [20] |
Petersen LB， Olsen KR， Christensen J，et al. Image and surgery-related costs comparing cone beam CT and panoramic imaging before removal of impacted mandibular third molars [J]. Dentomaxillofac Radiol， 2014， 43（6）：20140001. DOI： 10.1259/dmfr.20140001.
|
| [21] |
Kim YH， Lee C， Han SS，et al. Quantitative analysis of metal artifact reduction using the auto-edge counting method in cone-beam computed tomography [J]. Sci Rep， 2020， 10（1）：8872. DOI： 10.1038/s41598-020-65644-3.
|
| [22] |
Cheplygina V， de Bruijne M， Pluim JPW. Not-so-supervised：A survey of semi-supervised，multi-instance，and transfer learning in medical image analysis [J]. Med Image Anal， 2019， 54：280-296. DOI： 10.1016/j.media.2019.03.009.
|
| [23] |
Kim HE， Cosa-Linan A， Santhanam N，et al. Transfer learning for medical image classification：A literature review [J]. BMC Med Imaging， 2022， 22（1）：69. DOI： 10.1186/s12880-022-00793-7.
|
| [24] |
Shi M， Gong Z， Zeng P，et al. Multi-quantifying maxillofacial traits via a demographic parity-based AI model [J]. BME Front， 2024， 5：0054. DOI： 10.34133/bmef.0054.
|
| [25] |
Park CS， Kang SR， Kim JE，et al. Validation of bone mineral density measurement using quantitative CBCT image based on deep learning [J]. Sci Rep， 2023， 13（1）：11921. DOI： 10.1038/s41598-023-38943-8.
|
| [26] |
Bardhan S， Nagar G， Adapala K，et al. Validation of an AI model for automated detection of alveolar bone changes post-orthodontics using cone-beam computed tomography[J]. Cureus， 2025， 17（10）：e94809. DOI： 10.7759/cureus.94809.
|
| [27] |
Çoban G， Öztürk T， Hashimli N，et al. Comparison between cephalometric measurements using digital manual and web-based artificial intelligence cephalometric tracing software [J]. Dental Press J Orthod， 2022， 27（4）：e222112. DOI： 10.1590/2177-6709.27.4.e222112.oar.
|
| [28] |
Jeon S， Lee KC. Comparison of cephalometric measurements between conventional and automatic cephalometric analysis using convolutional neural network [J]. Prog Orthod， 2021， 22（1）：14. DOI： 10.1186/s40510-021-00358-4.
|
| [29] |
Cha JY， Yoon HI， Yeo IS，et al. Peri-implant bone loss measurement using a region-based convolutional neural network on dental periapical radiographs [J]. J Clin Med， 2021， 10（5）：1009. DOI： 10.3390/jcm10051009.
|
| [30] |
Hao J， Nalley A， Yeung AWK，et al. Characteristics，licensing，and ethical considerations of openly accessible oral-maxillofacial imaging datasets：A systematic review [J]. NPJ Digit Med， 2025， 8（1）：412. DOI： 10.1038/s41746-025-01818-5.
|
| [31] |
Vilcapoma P， Parra Meléndez D， Fernández A，et al. Comparison of Faster R-CNN，YOLO，and SSD for third molar angle detection in dental panoramic X-rays [J]. Sensors （Basel）， 2024， 24（18）：6053. DOI： 10.3390/s24186053.
|
| [32] |
Widiasri M， Arifin AZ， Suciati N，et al. Dental-YOLO：Alveolar bone and mandibular canal detection on cone beam computed tomography images for dental implant planning[J]. IEEE Access， 2022， 10：101483-101494. DOI： 10.1109/ACCESS.2022.3208350.
|
| [33] |
Chen CC， Wu YF， Aung LM，et al. Automatic recognition of teeth and periodontal bone loss measurement in digital radiographs using deep-learning artificial intelligence [J]. J Dent Sci， 2023， 18（3）：1301-1309. DOI： 10.1016/j.jds.2023.03.020.
|
| [34] |
Nguyen KT， Le BM， Li M，et al. Localization of cementoenamel junction in intraoral ultrasonographs with machine learning [J]. J Dent， 2021， 112：103752. DOI： 10.1016/j.jdent.2021.103752.
|
| [35] |
Liu T， Ye Y， Liu C，et al. Key-point based automated diagnosis for alveolar dehiscence in mandibular incisors using convolutional neural network [J]. Biomedical Signal Processing and Control， 2023， 85：105082. DOI： 10.1016/j.bspc.2023.105082.
|
| [36] |
Kazerouni A， Aghdam EK， Heidari M，et al. Diffusion models in medical imaging：A comprehensive survey [J]. Med Image Anal， 2023， 88：102846. DOI： 10.1016/j.media.2023.102846.
|
| [37] |
Te Lin Y， Li C， Korostoff J，et al. Three-dimensional digital quantitative analysis of periodontal and peri-implant phenotype：A narrative review [J]. Periodontol 2000， 2025. DOI： 10.1111/prd.12639.
|
| [38] |
Yang L， Zhu Z， Li Y，et al. Clinical-oriented 3D visualization and quantitative analysis of gingival thickness using convolutional neural networks and CBCT[J]. Front Dent Med， 2025， 6：1635155. DOI： 10.3389/fdmed.2025.1635155.
|
| [39] |
Chen YC， Chen L， Lai YL，et al. AI-driven detection and measurement of keratinized gingiva in dental photographs：Validation using reference retainers [J]. J Clin Periodontol， 2025， 52（7）：1056-1067. DOI： 10.1111/jcpe.14164.
|