Detection of incomplete root canal obturations in dental X-ray images via spatial-semantic attention and dynamic feature calibration

Publication Type

Conference Proceeding Article

Publication Date

10-2025

Abstract

To address the challenges of low resolution, loss of small target features, and interference from complex anatomical structures in detecting incomplete root canal obturations in dental periapical radiographs, this article proposes an improved YOLOv8 model. First, we design a Convolution module with Space-to-Depth Transformation (SDT-Conv) that preserves feature map resolution through spatial depth-wise separable convolutions, effectively mitigating loss of small targets caused by downsampling operations. Second, we construct a Dynamic Iterative Token Aggregator (DITA) architecture that enhances global feature representation through hyper-token spatial aggregation and semantic correlation, while employing a spatial-semantic dual-stream attention mechanism to strengthen multiscale feature fusion capabilities, thereby providing richer feature information for the entire network. Finally, we embed an Efficient Multiscale Attention (EMA) dynamic calibration mechanism in the detection head, which optimizes feature responses through cross-channel weight adaptation, enabling the model to precisely localize small object boundaries. The experimental results demonstrate that the improved model achieves 81.5% mAP@50 on the validation set, representing a 12.8% improvement over YOLOv8n. It effectively overcomes the challenges posed by variations in obturation materials, dental structure occlusions, and low-contrast interference.

Keywords

Irrigation, Adaptation models, Visualization, Convolution, Interference, Feature extraction, Dentistry, Calibration, Spatial resolution, X-ray imaging

Discipline

Artificial Intelligence and Robotics | Dentistry

Research Areas

Intelligent Systems and Optimization

Publication

Proceedings of the 2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Vienna, Austria, October 5-8

First Page

3274

Last Page

3279

Identifier

10.1109/SMC58881.2025.11342611

Publisher

IEEE

City or Country

Piscataway, NJ

Additional URL

https://doi.org/10.1109/SMC58881.2025.11342611

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