Increased risk of dental trauma in patients with allergic rhinitis: A nationwide population-based cohort study.

Allergic rhinitis (AR) is associated with various developmental issues that affecting dentition. We aimed to determine whether AR is associated with an increased risk of traumatic dental injuries (TDIs) in Taiwanese individuals. We used the Taiwan National Health Insurance Research Database (NHIRD) to conduct a nested case-control study. We compared an AR cohort with a matched cohort of patients without AR. New TDI cases were determined during our study period. To compare TDI risk between our study cohorts, we used Cox proportional regression analysis, and hazard ratios (HR) with 95% confidence intervals (CI) were calculated to quantify the association between AR exposure and TDI risk. In total, 76749 patients with AR (31715 male; 45034 female) were identified. In the AR and the non-AR cohorts, 312 patients in total had TDI. Patients with AR had a significantly higher risk of TDI than those without AR (aHR = 1.92; 95% CI = 1.459-2.525; P < 0.001). The risk of TDI was markedly higher in the AR cohort, except in the 3-12-year-old group, and with a CCI ≥ 1. AR patients had a future risk of TDI, indicating a potentially linked disease pathophysiology. The association between AR and TDI is greater among general patients. Clinicians and caregivers should be aware of potential TDI co-morbidity in patients with AR.

A benchmark for comparison of dental radiography analysis algorithms.

Dental radiography plays an important role in clinical diagnosis, treatment and surgery. In recent years, efforts have been made on developing computerized dental X-ray image analysis systems for clinical usages. A novel framework for objective evaluation of automatic dental radiography analysis algorithms has been established under the auspices of the IEEE International Symposium on Biomedical Imaging 2015 Bitewing Radiography Caries Detection Challenge and Cephalometric X-ray Image Analysis Challenge. In this article, we present the datasets, methods and results of the challenge and lay down the principles for future uses of this benchmark. The main contributions of the challenge include the creation of the dental anatomy data repository of bitewing radiographs, the creation of the anatomical abnormality classification data repository of cephalometric radiographs, and the definition of objective quantitative evaluation for comparison and ranking of the algorithms. With this benchmark, seven automatic methods for analysing cephalometric X-ray image and two automatic methods for detecting bitewing radiography caries have been compared, and detailed quantitative evaluation results are presented in this paper. Based on the quantitative evaluation results, we believe automatic dental radiography analysis is still a challenging and unsolved problem. The datasets and the evaluation software will be made available to the research community, further encouraging future developments in this field. (http://www-o.ntust.edu.tw/~cweiwang/ISBI2015/).