Transfer Learning From Micro‐ <scp>CT</scp> to Periapical Radiographs for Three‐Dimensional Root Canal Morphological Identification

Implicit 3D features extracted from micro-CT-based virtual radiographs can be effectively transferred to CSPRs through transfer learning. This approach enhances CNN interpretability and diagnostic precision in identifying root canal morphology. The benefits of transfer learning are greater for complex, multi-class tasks that require the extraction of intricate morphological features, whereas its effect diminishes in simplified binary classifications. These findings provide a theoretical and experimental foundation for applying multimodal transfer learning to clinical dental imaging.