Treffer: Scanner-agnostic dynamic jaw motion generation from virtual static excursive records using open-source Python-based artificial intelligence (AI) interpolation.
Title:
Scanner-agnostic dynamic jaw motion generation from virtual static excursive records using open-source Python-based artificial intelligence (AI) interpolation.
Authors:
Omar MS; Department of Prosthodontics, Indiana University School of Dentistry, Indianapolis, Indiana, USA., Yang CC; Department of Prosthodontics, Advanced Education Program in Prosthodontics, Indiana University School of Dentistry, Indianapolis, Indiana, USA., Morton D; ITI Scholarship Center, Center for Implant, Esthetic and Innovative Dentistry, Department of Prosthodontics, Indiana University School of Dentistry, Indianapolis, Indiana, USA., Lin WS; Department of Prosthodontics, Indiana University School of Dentistry, Indianapolis, Indiana, USA.
Source:
Journal of prosthodontics : official journal of the American College of Prosthodontists [J Prosthodont] 2025 Nov 14. Date of Electronic Publication: 2025 Nov 14.
Publication Model:
Ahead of Print
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Wiley-Blackwell Country of Publication: United States NLM ID: 9301275 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1532-849X (Electronic) Linking ISSN: 1059941X NLM ISO Abbreviation: J Prosthodont Subsets: MEDLINE
Imprint Name(s):
Publication: Hoboken, NJ : Wiley-Blackwell
Original Publication: Philadelphia, PA : W.B. Saunders Co., c1992-
Original Publication: Philadelphia, PA : W.B. Saunders Co., c1992-
References:
Harfagar G, Solis S, Hernandez M, Fehmer V, Sailer I, Azevedo L. Trueness and passivity of digital and conventional implant impressions in edentulous jaws: a prospective clinical study. Clin Implant Dent Relat Res. 2025;27(2):e70022.
Yehia A, Abo El Fadl A, El Sergany O, Ebeid K. Effect of different span lengths with different total occlusal convergences on the accuracy of intraoral scanners. J Prosthodont. 2024;33(3):252–258.
Nagai T, Liu W, Yang CC, Polido WD, Morton D, Lin WS. Intraoral scanning for implant‐supported complete‐arch fixed dental prostheses (ISCFDPs): four clinical reports. J Prosthodont. 2024. https://doi.org/10.1111/jopr.13971.
Chinam N, Bekkali M, Kallas M, Li J. Virtual occlusal records acquired by using intraoral scanners: a review of factors that influence maxillo‐mandibular relationship accuracy. J Prosthodont. 2023;32(Suppl 2):192–207.
Nagri D, Jain N, Dhawan P. Comparative assessment of the accuracy of digital versus conventional interocclusal records in dentulous patients: a systematic review. Cureus. 2024;16(10):e72002.
Ries JM, Grünler C, Wichmann M, Matta RE. Three‐dimensional analysis of the accuracy of conventional and completely digital interocclusal registration methods. J Prosthet Dent. 2022;128(5):994–1000.
Ren S, Morton D, Lin WS. Accuracy of virtual interocclusal records for partially edentulous patients. J Prosthet Dent. 2020;123(6):860–865.
Hsu MR, Driscoll CF, Romberg E, Masri R. Accuracy of dynamic virtual articulation: trueness and precision. J Prosthodont. 2019;28(4):436–443.
Koralakunte PR, Aljanakh M. The role of virtual articulator in prosthetic and restorative dentistry. J Clin Diagn Res. 2014;8(7):ZE25–ZE28.
Watanabe H, Fellows C, An H. Digital technologies for restorative dentistry. Dent Clin North Am. 2022;66(4):567–590.
Zhao W, Feng Y, Cao R, Sun J, Zhang J, Zhao X, et al. Comparative analysis of three jaw motion tracking systems: a study on precision and trueness. J Prosthodont. 2025;34(3):263–270.
Ahlers MO, Bernhardt O, Jakstat HA, Kordaß B, Türp JC, Schindler HJ, et al. Motion analysis of the mandible: guidelines for standardized analysis of computer‐assisted recording of condylar movements. Int J Comput Dent. 2015;18(3):201–223.
Revilla‐Leon M, Kois DE, Zeitler JM, Att W, Kois JC. An overview of the digital occlusion technologies: intraoral scanners, jaw tracking systems, and computerized occlusal analysis devices. J Esthet Restor Dent. 2023;35(5):735–744.
Kapper C, Müller L, Kronfeld A, Abello Mercado MA, Altmann S, Grauhan N, et al. Value of vendor‐agnostic deep learning image denoising in brain computed tomography: a multi‐scanner study. Rofo. 2025;197(1):65–75.
Tian IY, Wong MC, Kennedy S, Kelly NN, Liu YE, Garber AK, et al. A device‐agnostic shape model for automated body composition estimates from 3D optical scans. Med Phys. 2022;49(10):6395–6409.
Python Software Foundation. Downloads [Internet]. Python.org. [cited 2025 Apr 17]. Available from: https://www.python.org/downloads/.
Kluyver T, Ragan‐Kelley B, Pérez F, Granger B, Bussonnier M, Frederic J, et al. Jupyter notebooks—a publishing format for reproducible computational workflows. In: Loizides F, Schmidt B, editors. Positioning and power in academic publishing: players, agents and agendas. Amsterdam: IOS Press; 2016. p. 87–90.
Naik PG, Naik GR, Patil MB. Introduction to Google COLAB. In: Naik PG, Naik GR, Patil MB, editors. Conceptualizing Python in Google COLAB. Bilaspur (Chhattisgarh): Shashwat Publication; 2021. p. 1–32.
Yehia A, Abo El Fadl A, El Sergany O, Ebeid K. Effect of different span lengths with different total occlusal convergences on the accuracy of intraoral scanners. J Prosthodont. 2024;33(3):252–258.
Nagai T, Liu W, Yang CC, Polido WD, Morton D, Lin WS. Intraoral scanning for implant‐supported complete‐arch fixed dental prostheses (ISCFDPs): four clinical reports. J Prosthodont. 2024. https://doi.org/10.1111/jopr.13971.
Chinam N, Bekkali M, Kallas M, Li J. Virtual occlusal records acquired by using intraoral scanners: a review of factors that influence maxillo‐mandibular relationship accuracy. J Prosthodont. 2023;32(Suppl 2):192–207.
Nagri D, Jain N, Dhawan P. Comparative assessment of the accuracy of digital versus conventional interocclusal records in dentulous patients: a systematic review. Cureus. 2024;16(10):e72002.
Ries JM, Grünler C, Wichmann M, Matta RE. Three‐dimensional analysis of the accuracy of conventional and completely digital interocclusal registration methods. J Prosthet Dent. 2022;128(5):994–1000.
Ren S, Morton D, Lin WS. Accuracy of virtual interocclusal records for partially edentulous patients. J Prosthet Dent. 2020;123(6):860–865.
Hsu MR, Driscoll CF, Romberg E, Masri R. Accuracy of dynamic virtual articulation: trueness and precision. J Prosthodont. 2019;28(4):436–443.
Koralakunte PR, Aljanakh M. The role of virtual articulator in prosthetic and restorative dentistry. J Clin Diagn Res. 2014;8(7):ZE25–ZE28.
Watanabe H, Fellows C, An H. Digital technologies for restorative dentistry. Dent Clin North Am. 2022;66(4):567–590.
Zhao W, Feng Y, Cao R, Sun J, Zhang J, Zhao X, et al. Comparative analysis of three jaw motion tracking systems: a study on precision and trueness. J Prosthodont. 2025;34(3):263–270.
Ahlers MO, Bernhardt O, Jakstat HA, Kordaß B, Türp JC, Schindler HJ, et al. Motion analysis of the mandible: guidelines for standardized analysis of computer‐assisted recording of condylar movements. Int J Comput Dent. 2015;18(3):201–223.
Revilla‐Leon M, Kois DE, Zeitler JM, Att W, Kois JC. An overview of the digital occlusion technologies: intraoral scanners, jaw tracking systems, and computerized occlusal analysis devices. J Esthet Restor Dent. 2023;35(5):735–744.
Kapper C, Müller L, Kronfeld A, Abello Mercado MA, Altmann S, Grauhan N, et al. Value of vendor‐agnostic deep learning image denoising in brain computed tomography: a multi‐scanner study. Rofo. 2025;197(1):65–75.
Tian IY, Wong MC, Kennedy S, Kelly NN, Liu YE, Garber AK, et al. A device‐agnostic shape model for automated body composition estimates from 3D optical scans. Med Phys. 2022;49(10):6395–6409.
Python Software Foundation. Downloads [Internet]. Python.org. [cited 2025 Apr 17]. Available from: https://www.python.org/downloads/.
Kluyver T, Ragan‐Kelley B, Pérez F, Granger B, Bussonnier M, Frederic J, et al. Jupyter notebooks—a publishing format for reproducible computational workflows. In: Loizides F, Schmidt B, editors. Positioning and power in academic publishing: players, agents and agendas. Amsterdam: IOS Press; 2016. p. 87–90.
Naik PG, Naik GR, Patil MB. Introduction to Google COLAB. In: Naik PG, Naik GR, Patil MB, editors. Conceptualizing Python in Google COLAB. Bilaspur (Chhattisgarh): Shashwat Publication; 2021. p. 1–32.
Entry Date(s):
Date Created: 20251114 Latest Revision: 20251114
Update Code:
20251114
DOI:
10.1111/jopr.70051
PMID:
41236016
Database:
MEDLINE
Weitere Informationen
This technique describes a scanner-agnostic digital workflow for generating dynamic mandibular motion from static virtual interocclusal records using a custom artificial intelligence (AI) algorithm and user interface. Virtual records of mandibular positions, including maximum intercuspation, protrusion, and lateral excursions, were captured with an intraoral scanner and processed through a custom interface developed using Python, an open-source, script-based programming language. The program interpolates intermediate positions using quantified point tracking and exports a motion path file compatible with dental computer-aided design software. By leveraging AI and open-source tools, this method offers a cost-effective, non-vendor-specific solution for integrating individualized jaw motion into digital prosthodontic workflows.
(© 2025 The Author(s). Journal of Prosthodontics published by Wiley Periodicals LLC on behalf of American College of Prosthodontists.)