Background: Right colonic diverticulitis (RCD) is more common in Asian countries than in Western countries, and the risk factors for recurrence of RCD are not fully understood. The objective of this study was to assess the risk factors for recurrence of RCD. Methods: We analyzed 296 patients admitted for treatment of RCD in the Gachon University Gil Medical Center from December 2001 to October 2014. Gender, age, BMI, obesity, hypertension, diabetes mellitus, alcohol consumption, smoking, Hinchey classification, and hospital stay were investigated as risk factors for recurrence. Results: Of the 296 patients with RCD, 31 patients recurred after conservative treatment. The median time interval between the initial episode and recurrence of diverticulitis was 10.4 months. In the univariate analysis, a high recurrence rate was observed in patients with a history of alcohol consumption, smoking, and long hospital stay. In the multivariate analysis, the recurrence rate was much higher (p < 0.001) in patients who stayed in the hospital for more than 10 days after the first attack. Smoking also elevated the recurrence rate (p = 0.011). Conclusion: Factors associated with recurrence of RCD may include smoking and the long hospital stay due to complexity when first diverticulitis occurs. Further prospective large-scale studies are needed to draw a definite conclusion.

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Video prediction is the problem of generating future frames by exploiting the spatiotemporal correlation from the past frame sequence. It is one of the crucial issues in computer vision and has many real-world applications, mainly focused on predicting future scenarios to avoid undesirable outcomes. However, modeling future image content and object is challenging due to the dynamic evolution and complexity of the scene, such as occlusions, camera movements, delay and illumination. Direct frame synthesis or optical-flow estimation are common approaches used by researchers. However, researchers mainly focused on video prediction using one of the approaches. Both methods have limitations, such as direct frame synthesis, usually face blurry prediction due to complex pixel distributions in the scene, and optical-flow estimation, usually produce artifacts due to large object displacements or obstructions in the clip. In this paper, we constructed a deep neural network Frame Prediction Network (FPNet-OF) with multiplebranch inputs (optical flow and original frame) to predict the future video frame by adaptively fusing the future object-motion with the future frame generator. The key idea is to jointly optimize direct RGB frame synthesis and dense optical flow estimation to generate a superior video prediction network. Using various real-world datasets, we experimentally verify that our proposed framework can produce high-level video frame compared to other state-ofthe-art framework.

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