Deep learning for dentomaxillofacial cone-beam computed tomography enhancement: A systematic review and meta-analysis

BACKGROUND: Cone-beam computed tomography (CBCT) is widely used in dentomaxillofacial imaging and radiotherapy but is limited by noise and low contrast resolution. Deep learning (DL) has emerged as a promising tool for CBCT enhancement, improving image quality and diagnostic accuracy. This systematic review and meta-analysis evaluated DL-based techniques for dentomaxillofacial CBCT enhancement and their impact on objective image quality metrics. METHODS: statistics. RESULTS: > 99%) limits direct comparisons. CONCLUSION: DL improves CBCT image quality, but methodological variability and limited clinical validation necessitate further research.