ArticleThe Angle orthodontist2025
Validation of an AI-aided 3D method for enhanced volumetric quantification of external root resorption in orthodontics.
Article in The Angle orthodontist, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Artificial Intelligence in the Design and Optimization of Orthodontic Materials: A Clinical Perspective on Current State and Future Directions.Materials (Basel, Switzerland) · 2026Article
- Accuracy of artificial intelligence applications in periodontics: a thematic narrative review.Frontiers in dental medicine · 2026Review
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Authors and funding
3 authors.
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Abstract
Objectives: To compare and validate two tridimensional diagnostic methods for quantifying and categorizing external root resorption using an artificial intelligence (AI)-aided, automatic, or manual digital segmentation process. Materials and Methods: 40 teeth were segmented from 10 cone beam computed tomography (CBCT) records from five patients. Stereolithographic files were created, and automatic, manual, or AI-aided segmentation of each incisor was performed by two double-blinded operators. Two quantification methods were used and compared by analyzing final segmented regions of the tooth. This study followed QAREL (Quality Appraisal of Diagnostic Reliability) and COSMIN (COnsensus-based Standards for the selection of health Measurement Instruments) guidelines. Reproducibility was assessed using the Dahlberg formula, coefficient of variation, and intraclass correlation coefficient (ICC) ( Results: Intra- and interobserver correlations were high (ICC: > 0.736; Conclusions: Assessment of root resorption with CBCT is highly dependent on CBCT definition, type of segmentation, and measurement method. Three-dimensional (3D) measurement method described by three landmark points yielded satisfactory results using any tested segmentations.
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