Expertise-inspired artificial intelligence pipeline for clinically applicable reconstruction of tooth-centric radial planes: Development and multicenter validation

Abstract

Owing to the tooth-centered nature of most oral diseases, the tooth-centric radial plane of cone-beam computed tomography (CBCT) depicts the anatomical and pathological features along the long axis of the tooth, serving as a crucial imaging modality in the diagnosis, treatment planning, and prognosis of multiple oral diseases. However, reconstructing these standard planes from CBCT is labor-intensive, time-consuming, and error-prone due to anatomical variances and multi-center discrepancies. This study proposes an expertise-inspired artificial intelligence (AI) pipeline for the reconstruction of the tooth-centric radial plane. By emulating expert's workflow, this AI pipeline acquires the optimized maxillary and mandibular cross sections, segments the teeth for dental arch curve depiction, and reconstructs dental arch-defined tooth-centric radial planes. A total of 420 CBCT scans from two independent centers, comprising both healthy and diseased subjects, were collected for model development and validation. Teeth on the optimized cross sections were explicitly segmented even in the presence of various complex diseases, resulting in precise dental arch curve depictions. The AI-reconstructed tooth-centric radial planes for all teeth exhibited low angular and distance errors compared with the ground truth planes. In terms of clinical utility, the AI-reconstructed planes demonstrated high image quality, accurately represented anatomical and pathological features, and facilitated precise dental biometrics measurement by both clinicians and downstream AI diagnostic tools. The expertise-inspired AI pipeline showcases outstanding performance in reconstructing tooth-centric radial planes and offers significant clinical utility for intelligent oral health management with high interpretability, robustness and generalization capabilities.