Gastroenterology

Robust quantification of ICG fluorescence perfusion in neonatal bowel surgery via deep point tracking.

PURPOSE: Indocyanine green (ICG) fluorescence imaging is increasingly used for intraoperative bowel perfusion assessment in neonatal surgery. However, existing commercial solutions are limited to track few manually selected regions of interest (ROIs) and struggle with tissue motion and occlusion. We present a novel ICG quantification framework leveraging deep learning-based point tracking for robu...

A donor-recipient ranking model to optimize long-term survival post liver transplant.

Liver transplant (LT) is a life-saving treatment for patients with cirrhosis and/or hepatocellular carcinoma (HCC), but organ shortages and suboptimal donor-recipient matching remain major challenges and existing donor-recipient matching risk models offer limited predictive accuracy. Our study aims to develop a machine-learning-based model to predict and rank long-term post-transplant survival. We...

M²B-Net: a lightweight multi-scale multi-attention boundary-aware network for liver tumor segmentation from CT images.

Liver tumor segmentation from CT images remains challenging due to large variations in lesion scale, blurred boundaries, low tissue contrast, and the ...

Identifying hepatocellular carcinoma patients at risk of early non-response after first-cycle transarterial chemoembolization: A reproducible machine learning study.

PURPOSE: Transarterial chemoembolization (TACE) is standard therapy for intermediate-stage hepatocellular carcinoma (HCC), but early non-response afte...

Shared molecular signatures linking gastroesophageal reflux disease and major depressive disorder revealed by integrated machine learning.

BACKGROUND: Gastroesophageal reflux disease (GERD) and major depressive disorder (MDD) frequently co-occur, yet their shared molecular mechanisms rema...

Metabolic SERS Profiling under Triple-Coupled Hotspots Aided by Machine Learning for Precise Identification of Gastrointestinal Malignancies.

Serum metabolic analysis based on label-free SERS fingerprints represents a highly promising technique for the detection of tumors. However, the insuf...

The gut microbiome in preterm infants: development, dysbiosis, and disease implications.

SUMMARYPreterm infants face a unique trajectory of gut microbiome assembly, shaped by the convergence of physiological immaturity, necessary clinical ...

Investigating Immune Microenvironment of Inflammatory Bowel Disease and the Mitochondrial Metabolism-Related Molecular Mechanisms: Combining a Multiomics Approach and Experimental Validation.

Inflammatory bowel disease (IBD) is a chronic and recurrent gastrointestinal disease, the pathogenesis of which has not been fully elucidated. Increas...

Machine learning in the diagnosis of Hirschsprung disease: a systematic review and meta-analysis.

PURPOSE: To evaluate current evidence on machine learning (ML) for the diagnosis of Hirschsprung disease (HSCR) and summarize its diagnostic performan...

A myeloid immunosuppressive phenotype defines primary refractoriness to atezolizumab Plus bevacizumab in hepatocellular caarcinoma.

BACKGROUND: Despite improved outcomes with atezolizumab plus bevacizumab (A+B) in hepatocellular carcinoma (HCC), primary refractoriness (PRef), chara...

Network-informed multi-trait genomic analysis decodes the shared genetic architecture and therapeutic landscape of pelvic floor disorders.

Pelvic floor disorders, including pelvic organ prolapse and urinary incontinence, represent a common health burden with substantial clinical comorbidi...

Development and validation of a radiomics-dosiomics model for predicting radiotherapy response in locally advanced, unresectable non-small cell lung cancer: a multi-center study.

Early assessment of treatment response is essential for optimizing cancer management, as it allows timely interventions during the course of therapy, ...

Machine learning-based identification of a metabolic cell death gene signature for assessing disease activity and immunological landscape in inflammatory bowel disease.

Inflammatory bowel disease (IBD) is characterized by a refractory, relapsing inflammatory state driven by a multifaceted and poorly understood interpl...

Machine learning-driven automated evaluation of surgical skills during laparoscopic distal gastrectomy based on blood pixel analysis.

BACKGROUND: In laparoscopic distal gastrectomy (LDG), intraoperative bleeding directly reflects surgical proficiency, including tissue handling and he...

Fusion prediction model for post-ERCP pancreatitis under NSAIDs prophylaxis.

BACKGROUND: Endoscopic retrograde cholangiopancreatography (ERCP) is a common procedure for treating biliary and pancreatic disorders; however, it is ...

Non-invasive imaging diagnostic techniques in metabolic dysfunction-associated steatotic liver disease: a roadmap for clinicians.

Metabolic dysfunction-associated steatotic liver disease (MASLD), previously termed nonalcoholic fatty liver disease (NAFLD), is the most prevalent ch...

Integrated Multi-Omics Analysis and Experimental Validation Identify BCAT1 as a Critical Driver of Hepatocyte Pyroptosis in Acute-On-Chronic Liver Failure.

Acute-on-chronic liver failure (ACLF) is characterized by profound metabolic dysfunction and high mortality. Identifying amino acid metabolism-related...

A machine-learning-derived online screening tool for depressive symptoms in chronic digestive system diseases patients: a cross-sectional study with temporal validation from CHARLS.

Chronic digestive system diseases (CDSD) are common in older adults, and depressive symptoms are associated with poorer prognosis and quality of life....

Investigation of the molecular network underlying PET-MPs-induced inflammatory bowel disease via integrated machine learning and molecular docking approaches.

Polyethylene terephthalate microplastics (PET-MPs), as environmental contaminants, have raised significant concerns due to their potential toxicity, l...

Bridging prediction and reality: AI-based external validation of predictive models for lymph node metastasis in gastric cancer.

INTRODUCTION: The optimal extent of lymphadenectomy in gastric cancer surgery remains a subject of ongoing debate. Our previous modelling work indicat...