129 related articles for article (PubMed ID: 38830014)
21. Frontal cephalometric landmarking: humans vs artificial neural networks.
Muraev AA; Tsai P; Kibardin I; Oborotistov N; Shirayeva T; Ivanov S; Ivanov S; Guseynov N; Aleshina O; Bosykh Y; Safyanova E; Andreischev A; Rudoman S; Dolgalev A; Matyuta M; Karagodsky V; Tuturov N
Int J Comput Dent; 2020; 23(2):139-148. PubMed ID: 32555767
[TBL] [Abstract][Full Text] [Related]
22. Is automatic cephalometric software using artificial intelligence better than orthodontist experts in landmark identification?
Ye H; Cheng Z; Ungvijanpunya N; Chen W; Cao L; Gou Y
BMC Oral Health; 2023 Jul; 23(1):467. PubMed ID: 37422630
[TBL] [Abstract][Full Text] [Related]
23. Evaluation of AI Model for Cephalometric Landmark Classification (TG Dental).
Johannes T; Akhilanand C; Joachim K; Shankeeth V; Anahita H; Saeed Reza M; Mohammad B; Hossein MR
J Med Syst; 2023 Aug; 47(1):92. PubMed ID: 37615881
[TBL] [Abstract][Full Text] [Related]
24. The reliability of cephalometric measurements in oral and maxillofacial imaging: Cone beam computed tomography versus two-dimensional digital cephalograms.
Hariharan A; Diwakar NR; Jayanthi K; Hema HM; Deepukrishna S; Ghaste SR
Indian J Dent Res; 2016; 27(4):370-377. PubMed ID: 27723632
[TBL] [Abstract][Full Text] [Related]
25. Comparison between cephalometric measurements using digital manual and web-based artificial intelligence cephalometric tracing software.
Çoban G; Öztürk T; Hashimli N; Yağci A
Dental Press J Orthod; 2022; 27(4):e222112. PubMed ID: 35976288
[TBL] [Abstract][Full Text] [Related]
26. Evaluating the accuracy of automated cephalometric analysis based on artificial intelligence.
Bao H; Zhang K; Yu C; Li H; Cao D; Shu H; Liu L; Yan B
BMC Oral Health; 2023 Apr; 23(1):191. PubMed ID: 37005593
[TBL] [Abstract][Full Text] [Related]
27. Effectiveness of cone-beam computed tomography-generated cephalograms using artificial intelligence cephalometric analysis.
Chung EJ; Yang BE; Park IY; Yi S; On SW; Kim YH; Kang SH; Byun SH
Sci Rep; 2022 Nov; 12(1):20585. PubMed ID: 36446924
[TBL] [Abstract][Full Text] [Related]
28. Artificial intelligence-based analyses of varus leg alignment and after high tibial osteotomy show high accuracy and reproducibility.
Stotter C; Klestil T; Chen K; Hummer A; Salzlechner C; Angele P; Nehrer S
Knee Surg Sports Traumatol Arthrosc; 2023 Dec; 31(12):5885-5895. PubMed ID: 37975938
[TBL] [Abstract][Full Text] [Related]
29. Assessment of automatic cephalometric landmark identification using artificial intelligence.
Bulatova G; Kusnoto B; Grace V; Tsay TP; Avenetti DM; Sanchez FJC
Orthod Craniofac Res; 2021 Dec; 24 Suppl 2():37-42. PubMed ID: 34842346
[TBL] [Abstract][Full Text] [Related]
30. Reliability of mobile application-based cephalometric analysis for chair side evaluation of orthodontic patient in clinical practice.
Barbhuiya MH; Kumar P; Thakral R; Krishnapriya R; Bawa M
J Orthod Sci; 2021; 10():16. PubMed ID: 34568212
[TBL] [Abstract][Full Text] [Related]
31. Development of an artificial intelligence-based assessment model for prediction of embryo viability using static images captured by optical light microscopy during IVF.
VerMilyea M; Hall JMM; Diakiw SM; Johnston A; Nguyen T; Perugini D; Miller A; Picou A; Murphy AP; Perugini M
Hum Reprod; 2020 Apr; 35(4):770-784. PubMed ID: 32240301
[TBL] [Abstract][Full Text] [Related]
32. Comparison of AudaxCeph®'s fully automated cephalometric tracing technology to a semi-automated approach by human examiners.
Ristau B; Coreil M; Chapple A; Armbruster P; Ballard R
Int Orthod; 2022 Dec; 20(4):100691. PubMed ID: 36114136
[TBL] [Abstract][Full Text] [Related]
33. Artificial intelligence-based cephalometric landmark annotation and measurements according to Arnett's analysis: can we trust a bot to do that?
Silva TP; Hughes MM; Menezes LDS; de Melo MFB; Freitas PHL; Takeshita WM
Dentomaxillofac Radiol; 2022 Sep; 51(6):20200548. PubMed ID: 33882247
[TBL] [Abstract][Full Text] [Related]
34. Cephalometric measurements performed on CBCT and reconstructed lateral cephalograms: a cross-sectional study providing a quantitative approach of differences and bias.
Baldini B; Cavagnetto D; Baselli G; Sforza C; Tartaglia GM
BMC Oral Health; 2022 Mar; 22(1):98. PubMed ID: 35351080
[TBL] [Abstract][Full Text] [Related]
35. An artificial neural network approach for rational decision-making in borderline orthodontic cases: A preliminary analytical observational in silico study.
Kapoor S; Shyagali TR; Kuraria A; Gupta A; Tiwari A; Goyal P
J Orthod; 2023 Dec; 50(4):439-448. PubMed ID: 37148164
[TBL] [Abstract][Full Text] [Related]
36. Artificial Intelligence in Orthodontics: Critical Review.
Nordblom NF; Büttner M; Schwendicke F
J Dent Res; 2024 Jun; 103(6):577-584. PubMed ID: 38682436
[TBL] [Abstract][Full Text] [Related]
37. Comparison of Static and Dynamic Navigation in Root End Resection Performed by Experienced and Inexperienced Operators: An In Vitro Study.
Tang W; Jiang H
J Endod; 2023 Mar; 49(3):294-300. PubMed ID: 36528176
[TBL] [Abstract][Full Text] [Related]
38. [Comparative study of two software for the detection of cephalometric landmarks by artificial intelligence].
Moreno M; Gebeile-Chauty S
Orthod Fr; 2022 Mar; 93(1):41-61. PubMed ID: 35785943
[TBL] [Abstract][Full Text] [Related]
39. Development of novel artificial intelligence systems to predict facial morphology after orthognathic surgery and orthodontic treatment in Japanese patients.
Tanikawa C; Yamashiro T
Sci Rep; 2021 Aug; 11(1):15853. PubMed ID: 34349151
[TBL] [Abstract][Full Text] [Related]
40. Assessment of the quality of different commercial providers using artificial intelligence for automated cephalometric analysis compared to human orthodontic experts.
Kunz F; Stellzig-Eisenhauer A; Widmaier LM; Zeman F; Boldt J
J Orofac Orthop; 2023 Aug; ():. PubMed ID: 37642657
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]