Objective: To evaluate the generalizability of an artificial intelligence (AI)-based software for automated segmentation of

posterior teeth in cone beam CT (CBCT) scans and to identify the variables that influence the need for refinement of automatic

segmentations (AS).

Methods: A total of 190 scans from 190 patients, acquired using 5 CBCT systems were imported into the Virtual Patient

Creator (Relu, Leuven, Belgium) for AS. Two dental surgeons qualitatively assessed the segmentations of posterior teeth

and refined those requiring correction. Manual segmentation (MS) of 20% of the sample was performed using Mimics software

(Materialise, Leuven, Belgium). Performance was analyzed through voxel-by-voxel and surface-based comparisons,

in addition to the evaluation of time efficiency. Associations between independent variables and the need for refinement

were analyzed using mixed logistic regression (α = 5%).

Results: Among the 1005 teeth evaluated, only 12.7% required refinement. Age and the presence of brackets were significant

predictors (P<.001). The unexplained variability was attributed mainly to the patients, with minimal influence from

the CBCT systems. AS showed agreement with refined segmentations (R-AI) (IoU: 0.93-0.96; DSC: 0.96-0.98; precision:

0.99-1.00; recall: 0.94-0.96; accuracy: 0.98-0.99; MAD: 0.05-0.07; RMSE: 0.06-0.14) and excellent performance compared to

MS (IoU: 0.94; DSC: 0.97; precision: 0.98; recall: 0.95; accuracy: 0.98; MAD: 0.05; RMSE: 0.09). AS was more time-efficient

(12 [AIQ: 5]) compared to R-AI (202 [AIQ: 334]) and MS (1726 [AIQ: 863]).

Conclusion: The AI-based software demonstrated high accuracy and generalizability for automated segmentation of posterior

teeth in CBCT scans.

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