Data-Driven Modeling of Prosthetic Complications: A Literature Based Simulation and Machine Learning Approach in Prosthodontics

Year 2026, Volume: 53 Issue: 1, 18 - 22, 25.03.2026

Uğur Toprak

https://doi.org/10.52037/eads.2026.0004

https://izlik.org/JA53RF58JM

Abstract

Objective:
To assess how occlusion type, bruxism, and material characteristics influence prosthetic complications using a simulated dataset and machine learning models.

Methods:
A retrospective computational analysis was performed using a synthetic dataset of 1,000 simulated patients. Variables were based on clinical prevalence and prosthodontic literature, incorporating demographic, behavioral, and prosthetic parameters. Complication outcomes were generated using a literature-informed logistic risk model. Five supervised machine learning algorithms—Logistic Regression, Random Forest, Gradient Boosting, Support Vector Machine (SVM), and AdaBoost—were trained to predict prosthetic complications. Performance was evaluated using accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (ROC AUC).

Results:
Key factors associated with increased complication risk included sleep bruxism, implant-opposing dentition, use of PMMA material, and a greater number of prosthetic units. Logistic Regression yielded the highest overall predictive performance (ROC AUC = 0.625), though performance across models remained modest. The SVM model failed to detect complications, likely due to the imbalanced nature of the dataset. The limited predictive power of all models underscores the multifactorial etiology and complexity of prosthetic failure.

Conclusion:
This study demonstrates that machine learning, when applied to a realistically simulated dataset, can identify clinically plausible risk patterns for prosthetic complications. While predictive accuracy remains moderate, this approach offers a foundation for developing future clinical decision support tools. Incorporating synthetic modeling and machine learning may enhance risk stratification and personalized treatment planning in prosthodontics.

Keywords: Bruxism; Occlusion; Prosthetic Complications; Machine Learning; Simulated Data; Prosthodontics; Risk Modeling

Keywords

Bruxism , Occlusion , Prosthetic Complications , Machine Learning , Simulated Data , Prosthodontics , Risk Modeling

Thanks

The author gratefully acknowledges the guidance and support of Asst. Prof. Cengiz Evli, whose valuable insights and feedback contributed significantly to the development of this study.

References

• Bharali K, Das M, Nongthombam RS, Kumar A, Quazi S. OCCLUSAL CONSIDERATIONS IN IMPLANT DENTISTRY. Int J Med Biomed Stud. 2020;4(7). doi:10.32553/ijmbs.v4i7.1327.
• Chrcanovic BR, Kisch J, Larsson C. Retrospective clinical evaluation of 2- to 6-unit implant-supported fixed partial dentures: Mean follow-up of 9 years. Clin Implant Dent Relat Res. 2020;22(2):201–212. doi:10.1111/cid.12889.
• Coltro MPL, Ozkomur A, Villarinho EA, Teixeira ER, Vigo A, Shinkai RSA. Risk factor model of mechanical complications in implant-supported fixed complete dentures: A prospective cohort study. Clin Oral Implants Res. 2018;29(9):915–921. doi:10.1111/clr.13344.
• Chen Q, Luo S, Wang Y, Chen Z, Li Y, Meng M, et al. Three-dimensional finite element analysis of occlusal stress on maxillary first molars with different marginal morphologies restored with occlusal veneers. BMC Oral Health. 2024;24(1):1349. doi:10.1186/s12903-024-05121-9.
• Chrcanovic BR, Kisch J, Albrektsson T, Wennerberg A. Bruxism and dental implant failures: a multilevel mixed effects parametric survival analysis approach. J Oral Rehabil. 2016;43(11):813–823. doi:10.1111/joor.12431.
• Papaspyridakos P, Bordin TB, Kim YJ, El-Rafie K, Pagni SE, Natto ZS, et al. Technical Complications and Prosthesis Survival Rates with Implant-Supported Fixed Complete Dental Prostheses: A Retrospective Study with 1- to 12-Year Follow-Up. J Prosthodont. 2020;29(1):3–11. doi:10.1111/jopr.13119.
• Swaminathan Y, Rao G. Implant protected occlusion. IOSR J Dent Med Sci. 2013;11(3):20–25.
• Chrcanovic BR, Kisch J, Albrektsson T, Wennerberg A. Bruxism and dental implant treatment complications: a retrospective comparative study of 98 bruxer patients and a matched group. Clin Oral Implants Res. 2017;28(7):e1–e9. doi:10.1111/clr.12844.
• Koyano K, Esaki D. Occlusion on oral implants: current clinical guidelines. J Oral Rehabil. 2015;42(2):153–161. doi:10.1111/joor.12239.
• Bakopoulos A, Petridis H, Michalakis K, Tsalikis L, Vouros I. Clinical and Radiographic Changes at Implants Supporting Fixed Partial Dental Prostheses With Cantilever Extensions. A Retrospective Study After at Least 10 Years of Loading. Clin Oral Implants Res. 2025;36(10):1271–1286. doi:10.1111/clr.70000.
• Tallarico M, Lee Sy, Cho Yj, Noh Kt, Chikahiro O, Aguirre F, et al. Prosthetic Guidelines to Prevent Implant Fracture and Peri-Implantitis: A Consensus Statement from the Osstem Implant Community. Prosthesis. 2025;7(3):65. doi:10.3390/prosthesis7030065.
• Del Hougne M, Del Hougne P, Di Lorenzo I, Höhne C, Schrenker J, Schmitter M. Toward artificial intelligence in dental prosthesis planning - a preliminary in-silico feasibility study. BMC Oral Health. 2025;25(1):1386. doi:10.1186/s12903-025-06778-6.
• Brägger U, Aeschlimann S, Bürgin W, Hämmerle CH, Lang NP. Biological and technical complications and failures with fixed partial dentures (FPD) on implants and teeth after four to five years of function. Clin Oral Implants Res. 2001;12(1):26–34. doi:10.1034/j.1600-0501.2001.012001026.x.
• Choi SH, Kim JS, Cha JY, Hwang CJ. Effect of malocclusion severity on oral health-related quality of life and food intake ability in a Korean population. Am J Orthod Dentofacial Orthop. 2016;149(3):384–90. doi:10.1016/j.ajodo.2015.08.019.
• Goodacre CJ, Bernal G, Rungcharassaeng K, Kan JY. Clinical complications with implants and implant prostheses. J Prosthet Dent. 2003;90(2):121–132. doi:10.1016/s0022-3913(03)00212-9.
• Lobbezoo F, Ahlberg J, Glaros A, Kato T, Koyano K, Lavigne G, et al. Bruxism defined and graded: an international consensus. J Oral Rehabil. 2013;40(1):2–4. doi:10.1111/joor.12011.
• Manfredini D, Poggio CE, Lobbezoo F. Is bruxism a risk factor for dental implants? A systematic review of the literature. Clin Implant Dent Relat Res. 2014;16(3):460–469. doi:10.1111/cid.12015.
• Proffit WR, Fields H, Larson B, Sarver DM. Contemporary orthodontics-e-book: contemporary orthodontics-E-Book. Elsevier Health Sciences; 2018.
• Wittneben J, Buser D, Salvi GE, Bürgin W, Hicklin S, Brägger U. Complication and failure rates with implant-supported fixed dental prostheses and single crowns: A 10-year retrospective study. Clin Implant Dent Relat Res. 2014;16(3):356–364. doi:10.1111/cid.12066.
• Papaspyridakos P, Barizan Bordin T, Kim YJ, DeFuria C, Pagni SE, Chochlidakis K, et al. Implant survival rates and biologic complications with implant-supported fixed complete dental prostheses: A retrospective study with up to 12-year follow-up. Clin Oral Implants Res. 2018;29(8):881–893. doi:10.1111/clr.13340.
• Mai HY, Seo JM, Jung JK, Lee DH. Strategic use of CAD-CAM interim restoration for the recovery of the vertical dimension of occlusion in the posterior partially edentulous jaw. Appl Sci. 2020;10(21):7735. doi:10.3390/app10217735.