ArticleClinical oral implants research2025
Comparison of AI-Powered Tools for CBCT-Based Mandibular Incisive Canal Segmentation: A Validation Study.
Article in Clinical oral implants research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence-based segmentation of mandibular canal on cone-beam computed tomography: a systematic review and meta-analysis.Oral radiology · 2026Pooled it
- Fully automated deep learning for the identification and classification of dental implant brands from multimodal radiographic images.Clinical oral investigations · 2026Article
- Clinical Applicability of Artificial Intelligence-Driven Implant Planning and Surgical Guide Design in the Maxillary Esthetic Zone: A Registry-Based Cohort Study.Clinical oral implants research · 2026Article
- From pixels to precision: a narrative review of AI-driven 3D morphological analysis and digital twinning in alveolar cleft management.Translational pediatrics · 2026Review
- Understanding the Workflows in Non-Guided and Static Computer-Assisted Implant Surgery.Clinical and experimental dental research · 2026Review
- FDA-Approved AI Solutions in Dental Imaging: A Narrative Review of Applications, Evidence, and Outlook.International dental journal · 2026Review
- Accuracy of artificial intelligence applications in periodontics: a thematic narrative review.Frontiers in dental medicine · 2026Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
objectiveIdentification 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
methodsAn 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.
resultsThe 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).
conclusionThe 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.