BACKGROUND: The mortality burden of metabolic dysfunction-associated fatty liver disease (MAFLD) is rising, making it crucial to predict mortality and identify the factors influencing it. While advanced machine learning algorithms are gaining recogni...
PURPOSE: This study is aimed to develop and validate a machine learning model, which combined radiomics and clinical characteristics to predicting the definitive chemoradiotherapy (dCRT) treatment response in esophageal squamous cell carcinoma (ESCC)...
BACKGROUND AND AIM: Artificial intelligence (AI) networks offer significant potential for predicting immunotherapy outcomes in gastrointestinal cancers by analyzing genetic mutation profiles. Their application in prognosis remains underexplored. This...
BACKGROUND AND AIM: Colorectal cancer is among the most prevalent and deadliest cancers. Early prediction of metastasis in patients with colorectal cancer is crucial in preventing it from the advanced stages and enhancing the prognosis among these pa...
BACKGROUND: Metabolic dysfunction-associated fatty liver disease (MAFLD) is a common chronic liver disease and represents a significant public health issue. Nevertheless, current risk stratification methods remain inadequate. The study aimed to use m...
BACKGROUND: Gastric stromal tumors (GSTs) and gastric leiomyomas (GLs) represent the primary subtypes of gastric submucosal tumors (SMTs) characterized by distinct biological characteristics and treatment modalities. The accurate differentiation betw...
BACKGROUND: Cholestasis, characterized by impaired bile flow, impacts cognitive function through systemic mechanisms, including inflammation and metabolic dysregulation. Despite its significance, targeted predictive models for cognitive impairment in...
BACKGROUND: The objective was to develop a biological age prediction model (NC-BA) for the Chinese population to enrich the relevant studies in this population. And to investigate the association between accelerated age and NAFLD.
BACKGROUND: The aim of this study was to develop and internally validate an interpretable machine learning (ML) model for predicting the risk of hepatocellular carcinoma (HCC) in patients with chronic hepatitis B (CHB) infection.
OBJECTIVES: Computed tomography (CT) colonography is increasingly recognized as a valuable modality for diagnosing colorectal lesions, however, the interpretation workload remains challenging for physicians. Deep learning-based artificial intelligenc...