EndoPlanner: An adaptive planning framework for root canal therapy with graph-based endodontic landmark detection and inference-time refinement.

Abstract

Root canal therapy is a prevalent dental procedure for treating pulp infections; however, its complexity due to the intricate variability of root canal anatomies frequently results in procedural errors, excessive tooth structure loss, and suboptimal outcomes, especially in multi-rooted teeth. Although Guided Endodontics enhances precision and predictability, it depends on manual planning using non-dedicated software, rendering the process labor-intensive, subjective, and confined to single-rooted teeth, thereby limiting broader adoption. In this study, we introduce EndoPlanner, an adaptive and pioneering framework that streamlines preoperative planning for Guided Endodontics solely from cone-beam computed tomography scans, seamlessly integrating automated dental segmentation, endodontic landmark detection, access cavity design, and surgical template generation. Specifically, we propose a unified bottom-up multi-peak encoding and graph-based decoding paradigm for root canal landmark localization and topology recognition, augmented by an anatomy-aware loss for targeted heatmap optimization and a self-supervised inference-time refinement strategy to bolster generalization across diverse root canal morphologies. Additionally, a tunable, geometry-guided algorithm is devised for automatic drill path planning, incorporating minimally invasive principles. Extensive evaluations affirm the superior performance and robustness of our method, yielding an average MRE of 0.767 mm, with SDRs of 74.9% within the strict 1.0 mm safety margin and 94.3% within the broader 2.0 mm tolerance for landmark localization, while attaining clinically acceptable standards for access planning. Deployed as a 3D Slicer extension, it completes preoperative preparation in an average of ∼4 min, reducing time by up to 96.05% relative to traditional workflows. Moreover, successful in-vitro experiments and clinical cases validate the efficacy of patient-specific access solutions facilitated by our efficient, reproducible, and minimally invasive planning schemes, highlighting their potential for routine practice, particularly among less-experienced practitioners.