Diagnostic Accuracy of Artificial Intelligence Models for Detecting Odontogenic Keratocytes on CBCT Imaging: A Systematic Review and Meta-Analysis.

BACKGROUND AND AIMS: Differentiating odontogenic keratocyst (OKC) from other radiolucent jaw lesions like ameloblastoma is clinically important but radiographically difficult. Recent advances in artificial intelligence (AI) show promise for enhancing diagnosis using cone-beam computed tomography (CBCT). This study aims to systematically evaluate and meta-analyze the diagnostic accuracy of AI models in detecting OKCs on CBCT imaging. METHODS: A systematic review and meta-analysis was conducted according to PRISMA-DTA guidelines. Five electronic databases were searched through July 6, 2025. Studies employing AI models for OKC detection using CBCT were included. Methodological quality was assessed using QUADAS-2. Pooled estimates were computed using a random-effects model, with heterogeneity evaluated via I2 and meta-regression. The Eager test and funnel plot were employed to assess publication bias. RESULTS: Twelve studies were included. AI models demonstrated high diagnostic accuracy, characterized by a pooled sensitivity of 89% (95% CI: 79%-95%) and specificity of 92% (95% CI: 81%-97%), both exceeding 85%, along with a substantial diagnostic odds ratio (87.06) and a robust discriminative ability (AUC = 0.828). Deep learning (DL) models achieved higher sensitivity (91%) than machine learning (ML) models (86%), while ML models showed slightly higher specificity. Heterogeneity was substantial (I2 = 78%-93%). Publication year explained 57.2% of the variability in sensitivity. CONCLUSIONS: AI-based models, particularly CNN-based DL architectures, demonstrate clinically relevant diagnostic performance with high sensitivity, specificity, and diagnostic odds ratios, supporting their potential role as adjunctive tools in CBCT-based differentiation of OKCs from other odontogenic lesions.