Deep Learning for Automatic Hyoid Tracking in Videofluoroscopic Swallow Studies.

The hyoid bone excursion is one of the most important gauges of larynx elevation in swallowing, contributing to airway protection and bolus passage into the esophagus. However, the implications of various parameters of hyoid bone excursion, such as the horizontal or vertical displacement and velocity, remain elusive and raise the need for a tool providing automatic kinematics analysis. Several conventional and deep learning-based models have been applied automatically to track the hyoid bone, but previous methods either require partial manual localization or do not transform the trajectory by anatomic axis. This work describes a convolutional neural network-based algorithm featuring fully automatic hyoid bone localization and tracking and spine axis determination. The algorithm automatically estimates the hyoid bone trajectory and calculates several physical quantities, including the average velocity and displacement in horizontal or vertical anatomic axis. The model was trained in a dataset of 365 videos of videofluoroscopic swallowing from 189 patients in a tertiary medical center and tested using 44 videos from 44 patients with different dysphagia etiologies. The algorithm showed high detection rates for the hyoid bone. The results showed excellent inter-rater reliability for hyoid bone detection, good-to-excellent inter-rater reliability for calculating the maximal displacement and the average velocity of the hyoid bone in horizontal or vertical directions, and moderate-to-good reliability in calculating the average velocity in horizontal direction. The proposed algorithm allows for complete automatic kinematic analysis of hyoid bone excursion, providing a versatile tool with high potential for clinical applications.

The risk of fibromyalgia in patients with iron deficiency anemia: a nationwide population-based cohort study.

Since iron is essential for neurotransmitter synthesis, decreased iron stores might lead to reduced production of biogenic amines which phenomenon was shown in Fibromyalgia (FM) patients. The aims are to investigate the association of iron deficiency anemia (IDA) and FM and to find the effects of different interventions. We conducted a study using the Taiwan National Health Insurance Research Database. The IDA cohort consisted of 13,381 patients with newly diagnosed IDA between 2000 and 2008. Each patient with IDA was frequency-matched with one people without IDA, by sex, age and index year. The Cox proportional hazards regression analysis was conducted to estimate the association between IDA and FM risk. The event was the occurrence of FM. The overall incidence density rate of FM in the IDA cohort was higher than in the non-IDA cohort with a multivariable Cox proportional hazards model measured adjusted hazard ratio [HR], 1.19; 95% confidence interval [CI], 1.13-1.25). When using non-IDA group as reference, we compared with different therapies for IDA. The adjusted HRs of FM were 1.38 (95% CI = 1.30-1.47), 1.10 (95% CI = 1.03-1.16), 1.18 (95% CI = 0.98-1.43) and 0.73 (95% CI = 0.58-0.90) for IDA patient without therapy, iron supplement alone, blood transfusion alone and both iron supplement and blood transfusion respectively. Our results suggest IDA is associated with an increased risk of FM. All patients should have iron supplementation both to correct anemia and replenish body stores.