Detecting dental diseases through panoramic X-rays images is a standard procedure for dentists. Normally, a dentist need to identify diseases and find the infected teeth. While numerous machine learning models adopting this two-step procedure have been developed, there has not been an end-to-end model that can identify teeth and their associated diseases at the same time. To fill the gap, we develop YOLOrtho, a unified framework for teeth enumeration and dental disease detection. We develop our model on Dentex Challenge 2023 data, which consists of three distinct types of annotated data. The first part is labeled with quadrant, and the second part is labeled with quadrant and enumeration and the third part is labeled with quadrant, enumeration and disease. To further improve detection, we make use of Tufts Dental public dataset. To fully utilize the data and learn both teeth detection and disease identification simultaneously, we formulate diseases as attributes attached to their corresponding teeth. Due to the nature of position relation in teeth enumeration, We replace convolution layer with CoordConv in our model to provide more position information for the model. We also adjust the model architecture and insert one more upsampling layer in FPN in favor of large object detection. Finally, we propose a post-process strategy for teeth layout that corrects teeth enumeration based on linear sum assignment. Results from experiments show that our model exceeds large Diffusion-based model.

翻译：通过全景X光图像检测口腔疾病是牙科医生的标准流程。通常，牙医需要识别疾病并定位感染牙齿。尽管已有众多采用两步法的机器学习模型，但目前尚缺乏能够同时识别牙齿及其相关疾病的端到端模型。为填补这一空白，我们提出了YOLOrtho这一统一框架，用于牙齿编号与口腔疾病检测。模型基于Dentex Challenge 2023数据集开发，该数据集包含三种不同类型的标注数据：第一部分仅标注象限，第二部分标注象限与编号，第三部分标注象限、编号及疾病。为进一步提升检测性能，我们还使用了Tufts Dental公共数据集。为充分利用数据并同时学习牙齿检测与疾病识别，我们将疾病建模为对应牙齿的附加属性。针对牙齿编号的位置关系特性，我们在模型中用CoordConv替代传统卷积层以增强位置信息感知。同时调整模型架构，在FPN中增加一层上采样以优化大目标检测。最后提出基于线性求和分配的牙齿布局后处理策略，用于修正牙齿编号。实验结果表明，我们的模型超越了基于扩散的大型模型。