Trends and projections of inflammatory bowel disease at the global, regional and national levels, 1990-2050: a bayesian age-period-cohort modeling study.

BACKGROUND: Inflammatory bowel disease (IBD) is a global health concern with varying levels and trends across countries and regions. Understanding these differences is crucial for effective prevention and treatment strategies. METHODS: Using data from the 2019 Global Burden of Disease study, we examine IBD incidence, mortality, and disability-adjusted life years (DALYs) rates in 198 countries from 1990 to 2019. To assess changes in the burden of IBD, estimated annual percentage changes (EAPC) were calculated, and a Bayesian age-period-cohort model was used to predict the future 30-year trends of IBD. RESULTS: In 2019, there were 405,000 new IBD cases globally (95% uncertainty interval (UI) 361,000 to 457,000), with 41,000 deaths (95% UI 35,000 to 45,000) and 1.62million DALYs (95% UI 1.36-1.92million). The global age-standardized incidence rate in 2019 was 4.97 per 100,000 person-years (95% UI 4.43 to 5.59), with a mortality rate of 0.54 (95% UI 0.46 to 0.59) and DALYs rate of 20.15 (95% UI 16.86 to 23.71). From 1990 to 2019, EAPC values for incidence, mortality, and DALYs rates were - 0.60 (95% UI - 0.73 to - 0.48), - 0.69 (95% UI - 0.81 to - 0.57), and - 1.04 (95% UI - 1.06 to - 1.01), respectively. Overall, the burden of IBD has shown a slow decline in recent years. In SDI stratification, regions with higher initial SDI (high-income North America and Central Europe) witnessed decreasing incidence and mortality rates with increasing SDI, while regions with lower initial SDI (South Asia, Oceania, and Latin America) experienced a rapid rise in incidence but a decrease in mortality with increasing SDI. Predictions using a Bayesian model showed lower new cases and deaths from 2020 to 2050 than reference values, while the slope of the predicted incidence-time curve closely paralleled that of the 2019 data. CONCLUSION: Increasing cases, deaths, and DALYs highlight the sustained burden of IBD on public health. Developed countries have stabilized or declining incidence rates but face high prevalence and societal burden. Emerging and developing countries experience rising incidence. Understanding these changes aids policymakers in effectively addressing IBD challenges in different regions and economic contexts.

A deep learning method for foot-type classification using plantar pressure images.

Background: Flat foot deformity is a prevalent and challenging condition often leading to various clinical complications. Accurate identification of abnormal foot types is essential for appropriate interventions. Method: A dataset consisting of 1573 plantar pressure images from 125 individuals was collected. The performance of the You Only Look Once v5 (YOLO-v5) model, improved YOLO-v5 model, and multi-label classification model was evaluated for foot type identification using the collected images. A new dataset was also collected to verify and compare the models. Results: The multi-label classification algorithm based on ResNet-50 outperformed other algorithms. The improved YOLO-v5 model with Squeeze-and-Excitation (SE), the improved YOLO-v5 model with Convolutional Block Attention Module (CBAM), and the multilabel classification model based on ResNet-50 achieved an accuracy of 0.652, 0.717, and 0.826, respectively, which is significantly higher than those obtained using the ordinary plantar-pressure system and the standard YOLO-v5 model. Conclusion: These results indicate that the proposed DL-based multilabel classification model based on ResNet-50 is superior in flat foot type detection and can be used to evaluate the clinical rehabilitation status of patients with abnormal foot types and various foot pathologies when more data on patients with various diseases are available for training.