Related Experiment Video
Updated: Jan 18, 2026
Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
Published on: February 23, 2024
Comparison of AI-Powered Tools for CBCT-Based Mandibular Incisive Canal Segmentation: A Validation Study
Maria Fernanda Silva da Andrade-Bortoletto1,2, Thanatchaporn Jindanil2,3,4, Rocharles Cavalcante Fontenele2,5
1Department of Oral Diagnosis, Area of Dental Radiology, Piracicaba Dental School, State University of Campinas (UNICAMP), Piracicaba, São Paulo, Brazil.
Objective:
Identification of the mandibular incisive canal (MIC) prior to anterior implant placement is often challenging. The present study aimed to validate an enhanced artificial intelligence (AI)-driven model dedicated to automated segmentation of MIC on cone beam computed tomography (CBCT) scans and to compare its accuracy and time efficiency with simultaneous segmentation of both mandibular canal (MC) and MIC by either human experts or a previously trained AI model.
Materials And Methods:
An enhanced AI model was developed based on 100 CBCT scans using expert-optimized MIC segmentation within the Virtual Patient Creator platform. The performance of the enhanced AI model was tested against human experts and a previously trained AI model using another 40 CBCT scans. Performance metrics included intersection over union (IoU), dice similarity coefficient (DSC), recall, precision, accuracy, and root mean square error (RSME). Time efficiency was also evaluated.
Results:
The enhanced AI model had IoU of 93%, DSC of 93%, recall of 94%, precision of 93%, accuracy of 99%, and RMSE of 0.23 mm. These values were significantly higher than those of the previously trained AI model for all metrics, and for manual segmentation for IoU, DSC, recall, and accuracy (p < 0.0001). The enhanced AI model demonstrated significant time efficiency, completing segmentation in 17.6 s (125 times faster than manual segmentation) (p < 0.0001).
Conclusion:
The enhanced AI model proved to allow a unique and accurate automated MIC segmentation with high accuracy and time efficiency. Besides, its performance was superior to human expert segmentation and a previously trained AI model segmentation.
