Sonny Wong1, Han Deng2, Jaime Gateno3, Peng Yuan4, Fred A Garrett5, Randy K Ellis6, Jeryl D English7, Helder B Jacob8, Daeseung Kim2, James J Xia9. 1. Resident, Department of Orthodontics, The University of Texas Health Science Center at Houston School of Dentistry, Houston, TX. 2. Postdoctoral Fellow, Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, Houston, TX. 3. Chairman and Professor, Department of Oral and Maxillofacial Surgery, Houston Methodist, Houston, TX; Professor of Clinical Surgery (Oral and Maxillofacial Surgery), Joan & Sanford I. Weill Medical College of Cornell University, New York, NY. 4. Research Associate, Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, Houston, TX. 5. Clinical Professor, Department of Orthodontics, The University of Texas Health Science Center at Houston School of Dentistry, Houston, TX. 6. Clinical Associate Professor, Department of Orthodontics, The University of Texas Health Science Center at Houston School of Dentistry, Houston, TX. 7. Chairman and Professor, Department of Orthodontics, The University of Texas Health Science Center at Houston School of Dentistry, Houston, TX. 8. Assistant Professor, Department of Orthodontics, The University of Texas Health Science Center at Houston School of Dentistry, Houston, TX. 9. Professor, Department of Oral and Maxillofacial Surgery, and Director, Surgical Planning Laboratory, Houston Methodist, Houston, TX; and Professor of Surgery (Oral and Maxillofacial Surgery) Department of Surgery, Joan & Sanford I. Weill Medical College of Cornell University, New York, NY. Electronic address: JXia@HoustonMethodist.org.
Abstract
PURPOSE: Methods for digital dental alignment are not readily available to automatically articulate the upper and lower jaw models. The purpose of the present study was to assess the accuracy of our newly developed 3-stage automatic digital articulation approach by comparing it with the reference standard of orthodontist-articulated occlusion. MATERIALS AND METHODS: Thirty pairs of stone dental models from double-jaw orthognathic surgery patients who had undergone 1-piece Le Fort I osteotomy were used. Two experienced orthodontists manually articulated the models to their perceived final occlusion for surgery. Each pair of models was then scanned twice-while in the orthodontist-determined occlusion and again with the upper and lower models separated and positioned randomly. The separately scanned models were automatically articulated to the final occlusion using our 3-stage algorithm, resulting in an algorithm-articulated occlusion (experimental group). The models scanned together represented the manually articulated occlusion (control group). A qualitative evaluation was completed using a 3-point categorical scale by the same orthodontists, who were unaware of the methods used to articulate the models. A quantitative evaluation was also completed to determine whether any differences were present in the midline, canine, and molar relationships between the algorithm-determined and manually articulated occlusions using repeated measures analysis of variance (ANOVA). Finally, the mean ± standard deviation values were computed to determine the differences between the 2 methods. RESULTS: The results of the qualitative evaluation revealed that all the algorithm-articulated occlusions were as good as the manually articulated ones. The results of the repeated measures ANOVA found no statistically significant differences between the 2 methods [F(1,28) = 0.03; P = .87]. The mean differences between the 2 methods were all within 0.2 mm. CONCLUSIONS: The results of our study have demonstrated that dental models can be accurately, reliably, and automatically articulated using our 3-stage algorithm approach, meeting the reference standard of orthodontist-articulated occlusion.
PURPOSE: Methods for digital dental alignment are not readily available to automatically articulate the upper and lower jaw models. The purpose of the present study was to assess the accuracy of our newly developed 3-stage automatic digital articulation approach by comparing it with the reference standard of orthodontist-articulated occlusion. MATERIALS AND METHODS: Thirty pairs of stone dental models from double-jaw orthognathic surgery patients who had undergone 1-piece Le Fort I osteotomy were used. Two experienced orthodontists manually articulated the models to their perceived final occlusion for surgery. Each pair of models was then scanned twice-while in the orthodontist-determined occlusion and again with the upper and lower models separated and positioned randomly. The separately scanned models were automatically articulated to the final occlusion using our 3-stage algorithm, resulting in an algorithm-articulated occlusion (experimental group). The models scanned together represented the manually articulated occlusion (control group). A qualitative evaluation was completed using a 3-point categorical scale by the same orthodontists, who were unaware of the methods used to articulate the models. A quantitative evaluation was also completed to determine whether any differences were present in the midline, canine, and molar relationships between the algorithm-determined and manually articulated occlusions using repeated measures analysis of variance (ANOVA). Finally, the mean ± standard deviation values were computed to determine the differences between the 2 methods. RESULTS: The results of the qualitative evaluation revealed that all the algorithm-articulated occlusions were as good as the manually articulated ones. The results of the repeated measures ANOVA found no statistically significant differences between the 2 methods [F(1,28) = 0.03; P = .87]. The mean differences between the 2 methods were all within 0.2 mm. CONCLUSIONS: The results of our study have demonstrated that dental models can be accurately, reliably, and automatically articulated using our 3-stage algorithm approach, meeting the reference standard of orthodontist-articulated occlusion.
Authors: N Nadjmi; W Mollemans; A Daelemans; G Van Hemelen; F Schutyser; S Bergé Journal: Int J Oral Maxillofac Surg Date: 2010-03-11 Impact factor: 2.789
Authors: Yu-Bing Chang; James J Xia; Jaime Gateno; Zixiang Xiong; Xiaobo Zhou; Stephen T C Wong Journal: IEEE Trans Med Imaging Date: 2010-06-07 Impact factor: 10.048
Authors: Han Deng; Peng Yuan; Sonny Wong; Jaime Gateno; Fred A Garrett; Randy K Ellis; Jeryl D English; Helder B Jacob; Daeseung Kim; Joshua C Barber; William Chen; James J Xia Journal: Int J Comput Assist Radiol Surg Date: 2020-02-25 Impact factor: 2.924
Authors: James J Xia; Jaime Gateno; John F Teichgraeber; Andrew M Christensen; Robert E Lasky; Jeremy J Lemoine; Michael A K Liebschner Journal: J Oral Maxillofac Surg Date: 2007-02 Impact factor: 1.895
Authors: Sam Sheng-Pin Hsu; Jaime Gateno; R Bryan Bell; David L Hirsch; Michael R Markiewicz; John F Teichgraeber; Xiaobo Zhou; James J Xia Journal: J Oral Maxillofac Surg Date: 2012-06-12 Impact factor: 1.895
Authors: C J Frick; H H Deng; J D English; H B Jacob; T Kuang; M K Grissom; D Kim; J Gateno; J J Xia Journal: Int J Oral Maxillofac Surg Date: 2022-02-17 Impact factor: 2.986