Beyond the Ferrule Effect: Artificial Intelligence, Adhesive Biomaterials, and Structural Preservation for Predicting the Restorability of Endodontically Treated Teeth

Review Article

Abstract

The restoration of endodontically treated teeth is challenging because the long-term prognosis depends on the interaction of biological, biomechanical, and restorative factors. Although the ferrule effect has long guided restorability, emerging evidence supports a broader approach incorporating minimally invasive dentistry, adhesive biomaterials, and artificial intelligence. This narrative review examined their role in predicting restorability beyond the ferrule effect, synthesizing peer-reviewed studies from PubMed, Scopus, and Web of Science. Long-term restorability is determined by the interaction of structural preservation, biomechanical behavior, adhesive strategies, and patient-related factors rather than by the ferrule alone. Preservation of residual pericervical dentin consistently emerged as one of the strongest predictors of fracture resistance and longevity, while minimally invasive access, adhesive biomaterials, and endocrown approaches conserve sound tooth structure. Machine-learning models showed promising accuracy for estimating restorability, survival, and fracture risk, with revised FDI criteria enabling validation. Restorability assessment is shifting toward a holistic, individualized, evidence-based paradigm.