Title

Artificial intelligence-based prediction of prosthesis displacement under simulated functional movements: An in vitro study

Authors

Ravindra M. Chavda¹, Karthikeyan Vasudevan^2,*, Shreeja Shah¹, Pooja Arora³, Jinil Niravkumar Patel¹ & Vipin Arora⁴

Affiliation

¹Department of Prosthodontics and Crown & Bridge, AMC Dental College and Hospital, Ahmedabad, Gujarat, India; ²Department of Prosthodontics, SRM Dental College, Ramapuram, Tamil Nadu, Chennai; ³Department of Prosthodontics, Taif University, Kingdom of Saudi Arabia; ⁴Department of Restorative Dental Sciences, Taif University, Kingdom of Saudi Arabia; *Corresponding author

Email

Ravindra M. Chavda - E-mail: aravsoravi20413@gmail.com
Karthikeyan Vasudevan -E-mail: drkarthikvasudev@gmail.com
Shreeja Shah - E-mail: shahshreeja2605@gmail.com
Pooja Arora - E-mail: drpoojaprosthodontist@gmail.com
Jinil Niravkumar Patel - E-mail: drjinilpatel@gmail.com
Vipin Arora - E-mail: vipinendodontist@gmail.com

Article Type

Research Article

Date

Received September 1, 2026; Revised September 30, 2026; Accepted September 30, 2026, Published September 30, 2026
 

Abstract

Prosthesis displacement during function compromises comfort, retention and masticatory efficiency, yet routine prosthodontic assessment still depends largely on subjective clinical observation. Therefore, it is of interest to evaluate whether artificial intelligence could predict maxillary complete denture displacement under simulated functional movements using optical tracking and load-cycle data. Seventy-two maxillary denture bases fabricated with conventional heat-cure, milled PMMA and 3D-printed resin were tested on standardized edentulous models under vertical, lateral and protrusive movements with and without simulated salivary film. Mean displacement was lowest for milled PMMA with simulated saliva (0.41 ± 0.12 mm) and highest for 3D-printed resin under dry lateral loading (1.18 ± 0.26 mm; p<0.001), while the artificial intelligence model predicted high-displacement events with 90.3% accuracy and AUC 0.95. Thus, artificial intelligence-based optical tracking accurately predicted denture displacement patterns and may support objective evaluation of prosthesis retention under functional simulation.

Keywords

Prosthesis displacement, complete denture, artificial intelligence (AI), optical tracking, functional movements, digital prosthodontics, retention

Citation

Chavda et al. Bioinformation 22(9): 5522-5526 (2026)

Edited by

Ashwini Dhopte

ISSN

0973-2063

Publisher

Biomedical Informatics

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.