ArticleImaging science in dentistry2023
Diagnostic performance of artificial intelligence using cone-beam computed tomography imaging of the oral and maxillofacial region: A scoping review and meta-analysis.
Article in Imaging science in dentistry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Deep learning-based detection of the second mesiobuccal canal in maxillary first molars using cone-beam computed tomography.BMC oral health · 2026Article
- AI-Based Detection of Dental Features on CBCT: Dual-Layer Reliability Analysis.Diagnostics (Basel, Switzerland) · 2025Article
- Artificial Intelligence Used for Diagnosis in Facial Deformities: A Systematic Review.Journal of personalized medicine · 2024Review
- Periapical Lesions in Panoramic Radiography and CBCT Imaging-Assessment of AI's Diagnostic Accuracy.Journal of clinical medicine · 2024Article
- AI in Orthodontics: Revolutionizing Diagnostics and Treatment Planning-A Comprehensive Review.Journal of clinical medicine · 2024Review
- Cone beam computed tomography and artificial intelligence. ¿Where we are?Revista cientifica odontologica (Universidad Cientifica del Sur)Article
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Authors and funding
3 authors.
Funding
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Abstract
Purpose: The aim of this study was to conduct a scoping review and meta-analysis to provide overall estimates of the recall and precision of artificial intelligence for detection and segmentation using oral and maxillofacial cone-beam computed tomography (CBCT) scans. Materials and Methods: A literature search was done in Embase, PubMed, and Scopus through October 31, 2022 to identify studies that reported the recall and precision values of artificial intelligence systems using oral and maxillofacial CBCT images for the automatic detection or segmentation of anatomical landmarks or pathological lesions. Recall (sensitivity) indicates the percentage of certain structures that are correctly detected. Precision (positive predictive value) indicates the percentage of accurately identified structures out of all detected structures. The performance values were extracted and pooled, and the estimates were presented with 95% confidence intervals (CIs). Results: In total, 12 eligible studies were finally included. The overall pooled recall for artificial intelligence was 0.91 (95% CI: 0.87-0.94). In a subgroup analysis, the pooled recall was 0.88 (95% CI: 0.77-0.94) for detection and 0.92 (95% CI: 0.87-0.96) for segmentation. The overall pooled precision for artificial intelligence was 0.93 (95% CI: 0.88-0.95). A subgroup analysis showed that the pooled precision value was 0.90 (95% CI: 0.77-0.96) for detection and 0.94 (95% CI: 0.89-0.97) for segmentation. Conclusion: Excellent performance was found for artificial intelligence using oral and maxillofacial CBCT images.
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