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光图像检测牙科疾病是牙医的标准流程。通常，牙医需要识别疾病并定位受感染的牙齿。尽管已有众多采用两阶段流程的机器学习模型被开发出来，但目前尚无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，我们开发了YOLOrtho——一个用于牙齿编号与牙科疾病检测的统一框架。我们基于Dentex Challenge 2023数据构建模型，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。为进一步提升检测效果，我们利用了Tufts Dental公共数据集。为充分挖掘数据潜力并同时学习牙齿检测与疾病识别，我们将疾病视为对应牙齿的附加属性。鉴于牙齿编号中位置关系的特性，我们在模型中用CoordConv替代卷积层，以提供更多位置信息。同时调整模型架构，在FPN中增加一个上采样层以支持大目标检测。最后，我们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。