Kaewdech, Apichat

Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality worldwide, with early detection playing a crucial role in improving survival rates. Artificial intelligence (AI), particularly in medical image analysis, has emerged as a potential tool for HCC diagnosis and surveillance. Recent advancements in deep learning-driven medical imaging have demonstrated significant potential in enhancing early HCC detection, particularly in ultrasound (US)-based surveillance. This review provides a comprehensive analysis of the current landscape, challenges, and future directions of AI in HCC surveillance, with a specific focus on the application in US imaging. Additionally, it explores AI’s transformative potential in clinical practice and its implications for improving patient outcomes. We examine various AI models developed for HCC diagnosis, highlighting their strengths and limitations, with a particular emphasis on deep learning approaches. Among these, convolutional neural networks have shown notable success in detecting and characterising different focal liver lesions on B-mode US often outperforming conventional radiological assessments. Despite these advancements, several challenges hinder AI integration into clinical practice, including data heterogeneity, a lack of standardisation, concerns regarding model interpretability, regulatory constraints, and barriers to real-world clinical adoption. Addressing these issues necessitates the development of large, diverse, and high-quality data sets to enhance the robustness and generalisability of AI models. Emerging trends in AI for HCC surveillance, such as multimodal integration, explainable AI, and real-time diagnostics, offer promising advancements. These innovations have the potential to significantly improve the accuracy, efficiency, and clinical applicability of AI-driven HCC surveillance, ultimately contributing to enhanced patient outcomes.

ABSTRACTBackgroundMetabolic dysfunction‐associated steatotic liver disease (MASLD) affects over one third of the global population and is closely linked to gastrointestinal, oncological, and cardiovascular diseases. As the most common cause of steatotic liver disease (SLD), MASLD is often referred to as “fatty liver” in general health information searches. With the Internet becoming a primary source of healthcare information, this study aimed to evaluate global and regional public interest in steatotic liver disease using Google Trends data from January 2004 to December 2024.MethodsA comprehensive analysis was performed using Google Trends with the search term “fatty liver disease.” Interest levels were quantified using Google Trends indices, and temporal patterns were evaluated using average annual percent change (AAPC). Regional variations were assessed to identify geographic hotspots of public interest.ResultsGlobal interest in MASLD demonstrated significant growth (AAPC: 6.77%, 95% Confidence interval [CI]: 5.33–8.23), with a 264% increase from 2004 to 2024. Peak interest was observed in August 2024 (index: 100) compared to the lowest point in June 2007. Iran showed the highest country‐specific search volume (index: 100), while within the United States, West Virginia demonstrated the greatest relative interest (index: 100). Key topics associated with MASLD included “liver,” “symptoms,” “disease,” and “therapy.”ConclusionsSubstantial growth in global public interest in MASLD, with distinct temporal and geographical patterns, provides insights to develop targeted awareness campaigns and educational interventions, particularly in regions with low search volumes. Future research should examine the relationship between online search patterns and clinical outcomes in MASLD.

Background/Objective: Clostridioides difficile infection (CDI) is a common healthcare-associated ailment, presenting major health and economic challenges, especially for the elderly. Despite its prevalence, comprehensive data about CDI’s impact on the elderly are limited. Methods: This study used the Global Burden of Disease Study 2019 data to analyze CDI trends from 2000 to 2019, considering factors like sex, region, and sociodemographic index (SDI). Results: This study revealed that CDI caused approximately 18,181 deaths and 252,709 disability-adjusted life years (DALYs) among the elderly worldwide. The Americas showed the highest CDI burden, while the Eastern Mediterranean saw the steepest rate increase from 2000 to 2019. Regions with a high SDI also displayed substantial CDI impact. Conclusions: The escalating burden of CDI in the elderly, especially in high-SDI areas and the Americas, emphasizes an urgent need for targeted public health strategies.