Optimizing Dental Anomaly Detection: A Region-Specific AI Framework with Hierarchical Attention Mechanisms

Interpretation of dental panoramic radiographs which encompass all teeth as well as portions of the jaw and facial bones is critically important for preventive care and for devising appropriate treatment plans based on clinical findings. However, a high clinical workload or the absence of a specialist may compromise the accurate interpretation of even fundamental conditions, such as the detection of abnormalities. In such cases, artificial intelligence techniques can serve as valuable tools to enhance diagnostic accuracy. This research introduces a modified detection framework based on YOLOv11, incorporating two main architectural enhancements: the addition of a module designed to increase attention to specific regions, and improvements to the multi-scale blocks in the backbone of the network. The post-processing stage also employed methods capable of effectively distinguishing overlapping teeth. Experimental results demonstrate an improvement of over 7 percent in the F1-score compared to the baseline YOLOv11 architecture. The proposed model demonstrates competitive performance compared to models with similar architectures and exhibits satisfactory generalization on an independent dataset that was not utilized during training. Furthermore, relying on the real-time processing capability inherent to the YOLO framework, the proposed method can serve as an effective deep learning engine for integration into web software platforms and tools, enabling rapid and accurate dental radiograph analysis in clinical and telemedicine environments.