A fully automatic AI system for tooth and alveolar bone segmentation from cone-beam CT images

TLDR

Accurate delineation of individual teeth and alveolar bones from CBCT images is essential for precision dental healthcare. The paper presents an AI system for efficient, precise, and fully automatic segmentation of real‑patient CBCT images. The system is evaluated on a large multi‑center dataset of 4,215 patients and 4,938 CBCT scans. The system attains radiologist‑level accuracy (average Dice 91.5 % for teeth, 93.0 % for bone), is 500 times faster, and reliably handles challenging cases, indicating strong potential to enhance digital dentistry workflows.

Abstract

Abstract Accurate delineation of individual teeth and alveolar bones from dental cone-beam CT (CBCT) images is an essential step in digital dentistry for precision dental healthcare. In this paper, we present an AI system for efficient, precise, and fully automatic segmentation of real-patient CBCT images. Our AI system is evaluated on the largest dataset so far, i.e., using a dataset of 4,215 patients (with 4,938 CBCT scans) from 15 different centers. This fully automatic AI system achieves a segmentation accuracy comparable to experienced radiologists (e.g., 0.5% improvement in terms of average Dice similarity coefficient), while significant improvement in efficiency (i.e., 500 times faster). In addition, it consistently obtains accurate results on the challenging cases with variable dental abnormalities, with the average Dice scores of 91.5% and 93.0% for tooth and alveolar bone segmentation. These results demonstrate its potential as a powerful system to boost clinical workflows of digital dentistry.