A Unified Deep Learning Framework for Visual Diagnosis of Palatal Radicular Grooves in CBCT Scans: A Multicenter Validation Study

Key Points

• Accurate detection of palatal radicular grooves using deep learning algorithms is achieved, improving diagnostic capabilities.
• The model achieved a sensitivity of 92% and specificity of 87% in interpreting CBCT scans from multiple centers.
• Analysis involved a multicenter framework, focusing on validating the effectiveness of deep learning in real-world scenarios.
• Successful implementation may enhance visual diagnosis accuracy, though further analysis is required for comprehensive clinical adaptation.