Artificial intelligence-assisted prediction of instrument separation during root canal preparation based on canal anatomy and instrument stress

Abstract

Instrument separation remains one of the most challenging procedural complications during root canal
preparation, often compromising treatment efficiency, prognosis, and long-term clinical outcomes.
Recent advances in artificial intelligence (AI) have created new opportunities to predict the likelihood
of instrument fracture by integrating anatomical, mechanical, and procedural data before and during
endodontic treatment. AI models can analyze cone-beam computed tomography images to identify
complex canal morphology, including severe curvature, narrow canals, bifurcations, and calcifications,
while simultaneously incorporating information related to instrument design, alloy properties, kinematics,
and estimated mechanical stress. Machine learning and deep learning algorithms enable the identification
of high-risk clinical scenarios, allowing clinicians to select appropriate instruments, modify preparation
strategies, and minimize excessive torsional and cyclic fatigue. AI-assisted predictive systems also support
personalized treatment planning, improve procedural consistency, and reduce operator-dependent
variability through evidence-based decision support. Despite these advantages, widespread clinical
implementation requires robust multicenter datasets, standardized validation protocols, seamless
integration with digital endodontic workflows, and transparent model interpretation to ensure clinician
confidence and patient safety. As digital dentistry continues to evolve, AI-assisted prediction of instrument
separation has the potential to transform preventive risk assessment, optimize root canal preparation,
and enhance the precision, safety, and overall success of modern endodontic therapy.