Transfer Learning From Micro-CT to Periapical Radiographs for Three-Dimensional Root Canal Morphological Identification | JoVE Visualize

Area of Science:

Dental Imaging and Diagnostics
Machine Learning in Medicine
Radiology and Anatomy

Background:

Accurate identification of root canal morphology is crucial for endodontic treatment success.
Current methods for analyzing root canal anatomy rely on 2D radiographs, which can limit the visualization of complex 3D structures.
Multimodal transfer learning offers a potential solution for enhancing diagnostic capabilities by integrating data from different imaging modalities.

Purpose of the Study:

To investigate the transfer of implicit anatomical features from micro-computed tomography (micro-CT) to periapical radiographs.
To evaluate the effectiveness of multimodal transfer learning for 3D root canal morphology identification.
To assess the impact of task complexity on the performance of transfer learning models.

Main Methods:

Fused-rooted mandibular second molars (MSMs) were scanned using micro-CT to create virtual radiographs.
Clinically simulated periapical radiographs (CSPRs) were generated from ex vivo mandibles.
Four convolutional neural network (CNN) architectures were trained using different pre-training strategies, including models pre-trained on ImageNet and virtual radiographs.
Grad-CAM visualization was employed to interpret model attention, and results were compared with endodontic residents' performance.

Main Results:

CNNs pre-trained on virtual radiographs achieved higher accuracy (69.68%) in a three-class classification task compared to ImageNet-pretrained models (64.36%) and endodontic residents (61.17%).
Grad-CAM analysis showed that virtual radiograph-pretrained models focused on relevant root structures, unlike ImageNet-pretrained models.
In a simplified two-class task, performance differences between methods were not statistically significant, suggesting transfer learning benefits are greater for complex tasks.

Conclusions:

Implicit 3D features from micro-CT virtual radiographs can be effectively transferred to CSPRs via transfer learning.
This approach improves CNN interpretability and diagnostic accuracy for root canal morphology identification.
The efficacy of multimodal transfer learning is more pronounced in complex, multi-class classification tasks, providing a foundation for its application in clinical dental imaging.