Accurate detection of cephalometric landmarks is crucial for orthodontic diagnosis and treatment planning. Current landmark detection methods are mainly divided into heatmap-based and regression-based approaches. However, these methods often rely on parallel computation of multiple models to improve accuracy, significantly increasing the complexity of training and deployment. This paper presented a novel regression method that can simultaneously detect all cephalometric landmarks in high-resolution X-ray images. By leveraging the encoder module of Transformer, a dual-encoder model was designed to achieve coarse-to-fine localization of cephalometric landmarks. The entire model consisted of three main components: a feature extraction module, a reference encoder module, and a fine-tuning encoder module, responsible for feature extraction and fusion of X-ray images, coarse localization of cephalometric landmarks, and fine localization of landmarks, respectively. The model was fully end-to-end differentiable and could learn the intercorrelation relationships between cephalometric landmarks. Experimental results showed that the successful detection rate (SDR) of our algorithm was superior to other existing methods. It attained the highest 2 mm SDR of 89.51% on Test Set 1 of the ISBI2015 dataset and 90.68% on the test set of the ISBI2023 dataset. Meanwhile, it reduces memory consumption and enhances the model’s popularity and applicability, providing more reliable technical support for orthodontic diagnosis and treatment plan formulation.
ObjectiveTo analyze the causal relationship between the intake of cheese or tea and the risk of gastroesophageal reflux disease (GERD). MethodsUsing a two-sample Mendelian randomization approach, single nucleotide polymorphisms (SNPs) associated with milk or tea intake were used as instrumental variables. The causal effect of milk or tea intake on the risk of GERD was investigated using the MR Egger method, the weighted median method, the inverse-variance weighted (IVW) random-effects model, and the IVW fixed-effects model. Multivariable analysis was conducted using the MR Egger method, and leave-one-out sensitivity analysis was performed to validate the reliability of the data. ResultsCheese intake could reduce the occurrence of GERD [IVW random-effects model β=–1.010, 95%CI (0.265, 0.502), P<0.05], while tea intake could lead to the occurrence of GERD [IVW random-effects model β=0.288, 95%CI (1.062, 1.673), P<0.05]. ConclusionCheese intake may have a positive causal relationship with reducing the risk of GERD occurrence, while tea intake may have a positive causal relationship with increasing the risk of GERD occurrence.