  Accurate evaluation of the treatment result on X-ray images is a significant
and challenging step in root canal therapy since the incorrect interpretation
of the therapy results will hamper timely follow-up which is crucial to the
patients' treatment outcome. Nowadays, the evaluation is performed in a manual
manner, which is time-consuming, subjective, and error-prone. In this paper, we
aim to automate this process by leveraging the advances in computer vision and
artificial intelligence, to provide an objective and accurate method for root
canal therapy result assessment. A novel anatomy-guided multi-branch
Transformer (AGMB-Transformer) network is proposed, which first extracts a set
of anatomy features and then uses them to guide a multi-branch Transformer
network for evaluation. Specifically, we design a polynomial curve fitting
segmentation strategy with the help of landmark detection to extract the
anatomy features. Moreover, a branch fusion module and a multi-branch structure
including our progressive Transformer and Group Multi-Head Self-Attention
(GMHSA) are designed to focus on both global and local features for an accurate
diagnosis. To facilitate the research, we have collected a large-scale root
canal therapy evaluation dataset with 245 root canal therapy X-ray images, and
the experiment results show that our AGMB-Transformer can improve the diagnosis
accuracy from 57.96% to 90.20% compared with the baseline network. The proposed
AGMB-Transformer can achieve a highly accurate evaluation of root canal
therapy. To our best knowledge, our work is the first to perform automatic root
canal therapy evaluation and has important clinical value to reduce the
workload of endodontists.
