Predictive Modeling of Patient-Specific Surgical Outcomes in Dental Implants Using ML Techniques

Abstract

In this research, we propose a machine learning (ML)-based general framework for the prediction of the patient-specific surgical outcome of dental implantation. The model seeks to predict important outcomes – such as osseointegration success, healing times, and complication risks – by utilizing pre-operative clinical, radiographic, and demographic information. The dataset consists of historical data on over 1,000 implant cases, including information like bone density, implant size, surgical information, and systemic health history. The prediction ability of a number of ML algorithms (e.g., Random Forest, XGBoost, Support Vector Machines) were examined. The best model obtained general accuracy of 92% and high sensitivity/specificity in recognition of possible complications. Importance analysis identified bone quality, smoking, and implant angulation as important influences. With what we introduce in this work, a useful clinical decision-support model is brought that could potentially help improve personalized treatment planning and minimize postoperative risks in dental implantology.