Revolutionizing Root Canal Therapy: A Machine Learning- Based Approach to Canal Morphology Identification and Navigation
Abstract
Root canal therapy (RCT) is often challenged by the complex and variable morphology of dental canals, which can lead to procedural
errors and reduced treatment success. Recent advancements in machine learning (ML) offer promising solutions for improving
the accuracy of canal identification and navigation. This study explores a machine learning-based approach to analyze cone-beam
computed tomography (CBCT) scans and accurately classify canal morphologies. Using supervised learning algorithms, the model
predicts complex canal structures and integrates these predictions into navigation guidance systems for endodontic instrumentation.
Preliminary results demonstrate enhanced detection of intricate canal patterns, improved procedural efficiency, and reduced risk of
errors compared to conventional methods. This research highlights the potential of ML-assisted RCT to revolutionize clinical practice
by providing precise, data-driven guidance, ultimately improving patient outcomes. Future work will focus on real-time integration and
robotic-assisted endodontic procedures.
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