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.

通过全景 X 光影像来检测牙科疾病是牙医的标准程序，我们开发了 YOLOrtho，一个用于齿数目和牙科疾病检测的统一框架，通过牙齿位置关系、卷积层替换、模型架构调整、上采样和后处理策略等方法来提高检测性能。