Deep learning for dentomaxillofacial cone-beam computed tomography enhancement: A systematic review and meta-analysis
Abstract
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: A systematic search across six databases identified studies applying DL for CBCT enhancement. Performance metrics included mean absolute error (MAE), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and root mean squared error (RMSE). Risk of bias was assessed using the modified QUADAS-2 tool. Meta-analyses were performed using random-effects models, with effect sizes expressed as standardized mean differences (SMD) for MAE and RMSE and mean differences (MD) for PSNR and SSIM. Heterogeneity was evaluated using Cochran's Q and I2 statistics.
Results: Thirty-seven studies met inclusion criteria, covering CBCT-to-CT synthesis (27), metal artifact reduction (4), noise reduction (3), motion artifact reduction (1), and super-resolution (2). DL significantly reduced MAE (SMD: -4.24, 95% CI: -5.68 to -2.80, p 2 > 99%) limits direct comparisons.
Conclusion: DL improves CBCT image quality, but methodological variability and limited clinical validation necessitate further research.
How strong is the evidence?
Evidence Hub reads this as a meta-analysis, done in people. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
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