Optimization of Irrigant Dynamics Through Artificial Intelligence–Based Simulation Models in Endodontic Therapy

Abstract

The optimization of irrigant dynamics remains a critical challenge in achieving effective root canal disinfection and cleaning during endodontic therapy. Traditional irrigation systems, although improved through sonic and
ultrasonic technologies, still exhibit limitations in controlling irrigant flow, pressure distribution, and penetration within complex canal anatomies. This study explores the development and application of artificial intelligence
(AI)–based simulation models designed to optimize irrigant dynamics through predictive flow control and adaptive learning algorithms. By integrating computational fluid dynamics (CFD) with machine learning
architectures, such as convolutional neural networks (CNNs) and reinforcement learning, the model simulates real-time irrigant behavior under variable canal geometries and fluid viscosities. Data from 3D-scanned root
canal morphologies were used to train the simulation system, enhancing its capacity to predict turbulence zones, apical flow efficiency, and potential extrusion risks. The AI-driven model demonstrated improved accuracy
in fluid behavior prediction compared to conventional CFD simulations, suggesting its potential for clinical translation into smart irrigation systems. This innovation underscores a paradigm shift toward data-driven,
precision-based endodontic irrigation protocols that prioritize both efficacy and safety. Future integration of such models into endodontic devices could enable automated feedback systems, personalized irrigation strategies,
and enhanced patient outcomes in root canal therapy.