Improving performance robustness of subject-based brain segmentation software.

Purpose: Artificial intelligence (AI)-based image analysis tools to quantify the brain have become commercialized. However, insufficient data for learning and scanner specificity is a limitation for achieving high quality. In the present study, the performance of personalized brain segmentation software when applied to multicenter data using an AI model trained on data from a single institution was improved.

Association between shift work and obesity according to body fat percentage in Korean wage workers: data from the fourth and the fifth Korea National Health and Nutrition Examination Survey (KNHANES 2008-2011).

Background: Health problems in shift workers vary including obesity acting as a risk factor in cerebrovascular diseases. Recent studies have commonly determined the prevalence of obesity in shift workers on the basis of body mass index. The accuracy of BMI for diagnosing obesity are still limited apparently.