Temporal trends in colorectal cancer mortality rates (1999-2022) in the United States.

Colorectal cancer (CRC) ranks as the third leading cause of cancer-related deaths in the United States (U.S.). Our study aims to analyze CRC mortality patterns in the U.S., focusing on gender and age groups from 1999 to 2022. We analyzed Age-Adjusted Mortality Rates (AAMRs) for CRC-related deaths using the CDC Wide-ranging Online Data for Epidemiologic Research (CDC WONDER) database and assessed differences between age and sex. CRC-related mortality decreased significantly from 1999 to 2011 (-2.81% APC) and from 2011 to 2020 (-1.95% APC) but a not significant uptrend from 2020 to 2022 (2% APC). Males experienced a more significant decrease. Among age groups, crude mortality decreased until 2020, except in age group 45-54, which showed an annual increase in mortality of 0.9% from 2004 to 2022. Furthermore, individuals aged 75-84 and 85+ saw a nonsignificant annual increase of 1.8% and 4.5% from 2020 to 2022, respectively. Our study highlights a significant decline in age and gender-specific CRC-related mortality from 1999 to 2020. However, the worrisome uptrend observed in the younger age group of 45-54 emphasizes the importance of implementing targeted public health measures and evidence-based interventions.

Artificial intelligence in gastrointestinal endoscopy: a comprehensive review.

Integrating artificial intelligence (AI) into gastrointestinal (GI) endoscopy heralds a significant leap forward in managing GI disorders. AI-enabled applications, such as computer-aided detection and computer-aided diagnosis, have significantly advanced GI endoscopy, improving early detection, diagnosis and personalized treatment planning. AI algorithms have shown promise in the analysis of endoscopic data, critical in conditions with traditionally low diagnostic sensitivity, such as indeterminate biliary strictures and pancreatic cancer. Convolutional neural networks can markedly improve the diagnostic process when integrated with cholangioscopy or endoscopic ultrasound, especially in the detection of malignant biliary strictures and cholangiocarcinoma. AI's capacity to analyze complex image data and offer real-time feedback can streamline endoscopic procedures, reduce the need for invasive biopsies, and decrease associated adverse events. However, the clinical implementation of AI faces challenges, including data quality issues and the risk of overfitting, underscoring the need for further research and validation. As the technology matures, AI is poised to become an indispensable tool in the gastroenterologist's arsenal, necessitating the integration of robust, validated AI applications into routine clinical practice. Despite remarkable advances, challenges such as operator-dependent accuracy and the need for intricate examinations persist. This review delves into the transformative role of AI in enhancing endoscopic diagnostic accuracy, particularly highlighting its utility in the early detection and personalized treatment of GI diseases.