Clinical Prediction Models for Peri-Implantitis Through an Immunopathological Lens: A Systematic Review and Functional Meta-Synthesis of Machine Learning and Conventional Approaches

Peri-implantitis is a chronic inflammatory condition driven by dysregulated host immune responses, yet clinical risk assessment continues to rely on routinely collected clinical indicators. Clinical prediction models, including machine learning-based and conventional approaches, have been proposed to integrate these indicators for peri-implantitis risk stratification, but their conceptualization of immunopathological risk has not been systematically examined. This systematic review and functional meta-synthesis were conducted according to PRISMA 2020. Six eligible studies were included, comprising 1316 patients and 2438 dental implants. Four studies employed machine learning-based models, and two used conventional clinical prediction approaches. A functional meta-synthesis was performed to interpret how models integrate clinical predictors as surrogate manifestations of immune dysregulation. Additionally, an exploratory random-effects meta-analysis of area under the receiver operating characteristic curve (AUC) values was conducted where applicable. Discriminative performance ranged from moderate to high across studies, with overlapping AUC estimates between modeling paradigms. Despite methodological differences, both machine learning and conventional models converged on shared immunopathological constructs related to inflammatory burden, prior periodontal disease, plaque-related factors, and host systemic conditions. These findings support the clinical utility of immunopathologically informed prediction models for peri-implantitis and highlight the need for future studies incorporating external validation.