Analysis of the Generalizability of An Artificial Intelligence-Based Software for Tomographic Segmentation of Posterior Teeth-An External Validation Study.
Authors
Affiliations (7)
Affiliations (7)
- Department of Oral Diagnosis, Piracicaba Dental School, University of Campinas, Piracicaba, SP, Brazil.
- Dental Sciences Graduate Program, Federal University of Espírito Santo, Vitória, ES, Brazil.
- Department of Dentistry, University of Brasília, Brasília, DF, Brazil.
- Department of Propaedeutics and Integrated Clinic, Division of Oral Radiology, School of Dentistry, Federal University of Bahia, Salvador, BA, Brazil.
- Department of Stomatology, Public Oral Health and Forensic Dentistry, Division of Oral Radiology, School of Dentistry of Ribeirão Preto, University of São Paulo, Ribeirão Preto, Brazil.
- OMFS-IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven & Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Belgium, 3000, Leuven, Belgium.
- Department of Dental Medicine, Karolinska Institute, Stockholm, Sweden.
Abstract
To evaluate the generalizability of an artificial intelligence (AI)-based software for automated segmentation of posterior teeth in cone beam computed tomography (CBCT) scans and to identify the variables that influence the need for refinement of automatic segmentations (AS). A total of 190 scans from 190 patients, acquired using five 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%). Among the 1,005 teeth evaluated, only 12.7% required refinement. Age and the presence of brackets were significant predictors (p < 0.001). 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 (1,726 [AIQ: 863]). The AI-based software demonstrated high accuracy and generalizability for automated segmentation of posterior teeth in CBCT scans.