Title
Non-clinical determinants predicting implant longevity: Model development and validation
Authors
R. Antony Jebaraj1, Surya Singh2, Richa Acharya2, Dhanashree Kamble3, Shubham Shegokar4, Priti P. Shah5 & Nancy Jidiya5,*
Affiliation
1Department of Oral and Maxillofacial Surgery, Rajas Dental College and Hospital, Kavalkinaru, Tirunelveli, India; 2Department of Oral and Maxillofacial Surgery, College of Dental Science and Research Centre, Bopal, Gujarat, India; 3Department of Oral Medicine & Radiology, Nanded Rural Dental College and Research Center, India; 4Department of Orthodontics and Dentofacial Orthopedics, Nanded Rural Dental College and Research Centre, India; 5Department of Oral Medicine & Radiology, Faculty of Dental Science, Nadiad, Gujarat, India; *Corresponding author
R. Antony Jebaraj - E-mail: drantonyjebaraj@gmail.com
Surya Singh - E-mail: singhsurya794@gmail.com
Richa Acharya - E-mail: richa.acharya18@gmail.com
Dhanashree Kamble - E-mail: dhanashreekamble.dk@gmail.com
Shubham Shegokar - E-mail: drshubhamshegokar24@gmail.com
Priti P. Shah - E-mail: pritishah.fods@ddu.ac.in
Nancy Jidiya - E-mail: nancygediya30@gmail.com
Article Type
Research Article
Date
Received July 1, 2026; Revised July 31, 2026; Accepted July 31, 2026, Published July 31, 2026
Abstract
Accurate prediction of dental implant longevity remains difficult using readily available non-clinical patient factors alone. Therefore, it is of interest to evaluate the internally validated 5-year implant longevity models using demographic, systemic and behavioural and maintenance variables from 864 implants in 326 patients. Logistic Regression, Random Forest and Gradient Boosting Machine models were assessed using AUC, Brier score, calibration and decision-curve analysis. The 5-year failure rate was 18.2% and Gradient Boosting Machine showed the best performance with an AUC of 0.64 and Brier score of 0.179. Thus, data shows the non-clinical variables provide moderate predictive accuracy and may support early risk stratification and future multimodal prediction models.
Keywords
Dental implants, longevity, machine learning (ML), risk prediction, behavioral factors, modelling
Citation
Jebaraj et al. Bioinformation 22(7): 4060-4070 (2026)
Edited by
Hiroj Bagde
ISSN
0973-2063
Publisher
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.
