Prediction and risk factor analysis of dental implant failure using Cox regression and machine learning.

Dental implants are a well-established treatment for replacing missing teeth; however, reports of associated complications are increasing. Identifying risk factors and estimating the likelihood of failure can assist clinicians in treatment planning and follow-up care. In this study, we analyzed 543 implants placed in 256 patients with disabilities, monitored over an average of 8 years, during which 32 implant failures occurred. We first developed traditional statistical models using Cox regression, a well-established method for survival analysis. Then, we evaluated three modern machine-learning models capable of handling time-to-event outcomes: RSF, DeepSurv, and TabNet. Among the machine-learning approaches, the tree-based RSF demonstrated the highest predictive performance; however, it did not outperform the best Cox model after adjusting for several patient- and implant-related characteristics. Both RSF and Cox models consistently identified anterior implant placement and replacement of periodontitis-related tooth loss as the strongest predictors of failure. Our findings suggest that newer machine-learning tools do not inherently outperform conventional models, particularly in datasets with few events. The selection of analytical methods should be guided by dataset size and event frequency rather than the novelty of the algorithm.