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Published on: July 6, 2015
Diagnostic, predictive, and therapeutic approaches for impacted canines: a systematic review and meta-analysis
Wang Yi1, Johari Yap Abdullah2,3, Umi Mardhiyyah Mat Ali1,4
1School of Dental Sciences, Universiti Sains Malaysia, Kampus Kesihatan, Kubang Kerian, 16150, Kelantan, Malaysia.
Objective:
Maxillary canine impaction affects approximately 1-3% of the population and presents diagnostic, prognostic, and therapeutic challenges. The clinical utility of artificial intelligence (AI) in this context remains uncertain. We synthesized evidence on diagnostic, predictive, and therapeutic approaches and situated AI-based methods alongside established strategies.
Methods:
PRISMA 2020-guided searches of PubMed, the Cochrane Library, Web of Science, and Embase (2015-2024) identified 28 eligible studies, grouped into diagnostic, predictive, and therapeutic domains. Risk of bias was assessed using domain-appropriate instruments. Meta-analyses were conducted by domain (no cross-domain pooled effect); diagnostic accuracy synthesis used hierarchical bivariate/HSROC models to accommodate threshold variability.
Results:
Diagnostic performance was moderate-to-substantial. CBCT was associated with higher diagnostic accuracy than two-dimensional panoramic radiography (OR 3.13, 95% CI 2.34-4.94; I² = 0.0%, p = 0.121). AI-related diagnostic estimates were reported as higher in a small subset of studies; however, limited sample sizes, heterogeneity, scarce external validation, and inconsistent reporting preclude clinical inference and warrant cautious interpretation. Predictive findings highlighted spatial parameters, particularly inter-tooth/inter-root contact distance. Therapeutic evidence suggested potential benefit of active interceptive interventions over conservative management, but most studies were short-term, heterogeneous, and predominantly non-randomized.
Conclusions:
CBCT and reproducible spatial indicators provide a practical framework, with imaging guided by radiation protection principles (ALARA). Current evidence does not support AI-based methods as clinically decisive; at best, they may serve as adjunctive, hypothesis-generating tools. Larger prospective studies with longer follow-up, standardized reporting, and rigorous external validation and calibration are needed.
Clinical Trial Registration:
PROSPERO Registration Number: CRD420251239971.
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