Increased risk of perforated appendicitis in patients with schizophrenia and dementia: A population-based case-control study.

Previous studies have suggested that patients with psychotic or mental disorders are relatively pain insensitive, resulting in difficulties in the diagnosis of acute intra-abdominal diseases requiring emergency surgeries. We aimed to evaluate whether central nervous system (CNS) or mental disorders are associated with perforated appendicitis in patients with acute appendicitis.We conducted a population-based case-control study using Taiwan's National Health Insurance Research database. Patients aged >18 years who had been hospitalized with a diagnosis of acute appendicitis between 2000 and 2013 were identified. After 1:1 matching for age and sex, 2792 patients with perforated appendicitis (case group) and 2792 patients with nonperforated appendicitis (control group) were included. CNS disorders, mental disorders, pain control medication, and several comorbidities were analyzed for the odds of appendiceal perforation with 95% confidence interval (CI) using the multivariable logistic regression model.Schizophrenia and dementia were associated with a high risk of appendiceal rupture in patients with acute appendicitis, with an adjusted odds ratio of 2.01 for dementia (95% CI: 1.19-3.39, P = .009) and 4.8 for schizophrenia (95% CI: 1.62-14.19, P = .005). Other factors, such as other CNS disorders, comorbidities, and pain control medication, were not associated with the risk of perforated appendicitis.Dementia and schizophrenia are associated with perforated appendicitis in patients with acute appendicitis. This might be owing to altered pain perception, difficult symptom expression, and delayed hospitalization. Further studies are still needed to determine the underlying mechanism and confirm the causality.

An Adaptive Exposure Fusion Method Using fuzzy Logic and Multivariate Normal Conditional Random Fields.

High dynamic range (HDR) has wide applications involving intelligent vision sensing which includes enhanced electronic imaging, smart surveillance, self-driving cars, intelligent medical diagnosis, etc. Exposure fusion is an essential HDR technique which fuses different exposures of the same scene into an HDR-like image. However, determining the appropriate fusion weights is difficult because each differently exposed image only contains a subset of the scene's details. When blending, the problem of local color inconsistency is more challenging; thus, it often requires manual tuning to avoid image artifacts. To address this problem, we present an adaptive coarse-to-fine searching approach to find the optimal fusion weights. In the coarse-tuning stage, fuzzy logic is used to efficiently decide the initial weights. In the fine-tuning stage, the multivariate normal conditional random field model is used to adjust the fuzzy-based initial weights which allows us to consider both intra- and inter-image information in the data. Moreover, a multiscale enhanced fusion scheme is proposed to blend input images when maintaining the details in each scale-level. The proposed fuzzy-based MNCRF (Multivariate Normal Conditional Random Fields) fusion method provided a smoother blending result and a more natural look. Meanwhile, the details in the highlighted and dark regions were preserved simultaneously. The experimental results demonstrated that our work outperformed the state-of-the-art methods not only in several objective quality measures but also in a user study analysis.