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

Abstract

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.