Latest AI and machine learning research in gastroenterology for healthcare professionals.
BACKGROUND: Disulfidptosis is a novel form of glucose starvation-induced cell death, yet its prognostic implications in gastric cancer (GC) remain largely undefined. METHODS: We integrated single-cell and bulk multi-omics data to identify core disulfidptosis-related genes. A robust Cox regression-based prognostic model was constructed. Machine learning combined with single-cell interaction analyse...
High-resolution spatial transcriptomics requires computational methods to accurately assign transcripts to individual cells. We present SMURF (Segmentation and Manifold UnRolling Framework), a cross-platform soft-segmentation algorithm that uses deep learning to map mRNAs from capture spots to nearby nuclei. SMURF also unrolls complex tissue architectures by projecting cells onto Cartesian coordin...
Due to its high genomic heterogeneity and dense desmoplastic microenvironment, traditional diagnosis and treatment for pancreatic ductal adenocarcinom...
The pancreas is local in the retroperitoneal space,in close proximity to critical blood vessels such as celiac trunk, superior mesenteric artery, hepa...
Postoperative pulmonary infection (PPI) after esophageal cancer surgery occurs frequently and severely impairs patients' prognosis. Most existing pred...
BACKGROUND: Barrett's oesophagus (BE), the precursor to oesophageal adenocarcinoma, progresses through a stepwise dysplastic sequence. Accurate dyspla...
BACKGROUND AND AIMS: High-quality, annotated datasets are fundamental to clinical research and artificial intelligence (AI) model development. Existin...
Depression and non-alcoholic fatty liver disease (NAFLD) are increasingly recognized as interconnected disorders, yet the causal mechanisms linking th...
BACKGROUND: To develop and validate a deep learning (DL) model based on feature fusion with B-mode ultrasound (BMUS) and contrast enhanced ultrasound ...
This study aimed to develop a robust prediction model- using machine-learning algorithms based on the core indicators of the tumor immune microenviron...
BACKGROUND: Research on the early detection of pancreatic cancer has grown rapidly in recent years; however, existing bibliometric studies in this fie...
Traditional TNM staging inadequately captures the recurrence risk of rectal cancer (RC), limiting prognostic accuracy and personalized treatment decis...
OBJECTIVE: To develop and validate a machine learning (ML) algorithm for predicting 30-day mortality in adult patients with acute pancreatitis (AP) us...
OBJECTIVE: Comprehensive identification and prioritization of developed artificial intelligence methods for applications of radiology can help to sele...
BACKGROUND: Medical education relies on experience to develop clinical reasoning skills, yet patient access is often limited. Large language models (L...
BACKGROUND: Chronic liver disease (CLD) and its progression to compensated cirrhosis (CC), decompensated cirrhosis (DC), and acute-on-chronic liver fa...
Metabolic dysfunction-associated steatotic liver disease (MASLD) is highly prevalent yet often underdiagnosed or undertreated in primary care due to a...
BACKGROUND: The selection of appropriate machine learning (ML) methods for clinical research remains challenging, particularly when both predictive pe...
BACKGROUND: Primary biliary cholangitis (PBC) management remains limited by reliance on static biochemical markers, fragmented assessment of symptom b...
Human liver transplantation is constrained by a critical shortage of viable donor livers. In response to this shortage, marginal livers from extended ...