Gastroenterology

Inflammatory marker-driven deep learning model for postoperative gastric cancer prognosis.

BACKGROUND: Prognostic prediction following gastric cancer surgery plays a pivotal role in postoperative management, helping to optimize therapeutic strategies and improve patient survival. Standard clinicopathological indicators, including tumor differentiation and lymph node metastasis, continue to serve as the basis for outcome evaluation; however, they do not adequately represent the host's sy...

Deep Learning-Based Standard Section Recognition and Multi-Organ Segmentation in Upper Abdominal Ultrasound.

OBJECTIVE: To develop and evaluate deep learning models for upper abdominal ultrasound standard section recognition and downstream multi-organ segmentation. METHODS: This retrospective study used real-world clinical ultrasound data from a single center. A total of 521 upper abdominal ultrasound examination videos were collected, of which 465 met the inclusion criteria. Standard section frames were...

Tissue identity is the dominant determinant of cross-species transferability of a porcine developmental programme.

BACKGROUND: Reproducibility and cross-species translation using the domestic pig (Sus scrofa) are limited by the lack of a standardised molecular fram...

Leveraging data-driven risk-adapted surveillance strategies after disease progression in locally advanced rectal cancer.

BACKGROUND: Post-progression survival (PPS) is a critical endpoint in oncology, yet predictors of PPS and data-driven strategies for post-progression ...

ESD-VesNet: uncertainty-aware vessel segmentation network for endoscopic submucosal dissection with hard negative mining.

PURPOSE: Preventing intraoperative bleeding in endoscopic submucosal dissection (ESD) relies on the timely identification and pre-coagulation of submu...

Comprehensive identification of three key genes and three key DNA methylation CpG sites associated with prognosis and prediction in colon adenocarcinoma.

Colon adenocarcinoma (COAD) is a common cancer with high incidence and mortality rates worldwide. DNA methylation has a significant impact on the occu...

Development of a predictive model for postoperative body mass index and diabetes outcomes after metabolic bariatric surgery: retrospective cohort study.

BACKGROUND: Predicting postoperative body mass index (BMI) trajectories and long-term type 2 diabetes (T2D) remission after bariatric surgery remains ...

Reply to: From traditional to artificial intelligence-driven approaches: Revolutionizing personalized and precision nutrition in inflammatory bowel disease.

Homogeneity of Liver Fat Distribution Serves as a Diagnostic Marker for Metabolic Dysfunction-Associated Steatohepatitis.

RATIONALE AND OBJECTIVES: To characterize liver fat distribution in metabolic dysfunction-associated steatohepatitis (MASH) and propose a magnetic res...

Pan-cancer analysis of tissue-plasma genomic concordance reveals enhanced concordance with liver metastasis and plasma clonality as predictor of poorer overall survival.

Circulating tumor DNA (ctDNA) enables noninvasive tumor genotyping, yet its concordance with tissue remains unclear. Using a 437-gene panel in 1111 pa...

Improving pancreatic adenocarcinoma prognosis models through ligand receptor interactions and histopathological integration.

BACKGROUND: Pancreatic adenocarcinoma (PAAD) has an extremely poor prognosis, and existing prognostic markers fail to fully capture the complex hetero...

Machine learning-assisted prediction of obstructive sleep apnea in patients with metabolic dysfunction-associated steatotic liver disease: a multicenter study with external validation.

BACKGROUND: Metabolic dysfunction-associated steatotic liver disease (MASLD) is closely associated with obstructive sleep apnea (OSA) through shared m...

Integrating WGCNA and machine learning to identify and validate key biomarkers in MASLD.

BACKGROUND: Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) represents the most prevalent chronic liver disease worldwide. The absenc...

The role of large language models in the writing of surgical reviews: fact or fantasy?

BACKGROUND: Amidst the current enthusiasm concerning artificial intelligence and its possible application in the composition of different kinds of sci...

Hepatitis B virus spreading via Beddington-DeAngelis incidence function and feed-forward neural network with optimal control.

Using an appropriate mathematical model, this study aims to examine the transmission dynamics and optimal control of hepatitis B virus spreading, empl...

Single-session agreement of ChatGPT and Gemini treatment recommendations with multidisciplinary tumor board decisions in thoracic oncology.

BACKGROUND: To evaluate the single-session agreement between treatment recommendations generated by ChatGPT and Gemini and real-world decisions made b...

Technical and clinical feasibility of single-use gastroscopy with real-time AI-based quality monitoring and single-use colonoscopy: a prospective two-center study.

BACKGROUND: Single-use gastrointestinal endoscopes eliminate the need for post-procedure reprocessing and have become an area of interest in endoscopi...

Radiomics-Based AI for the Diagnosis and Prognosis of Vessels Encapsulating Tumor Clusters in Hepatocellular Carcinoma: Systematic Review and Meta-Analysis.

BACKGROUND: Vessels encapsulating tumor clusters (VETCs), a CD34-positive vascular pattern in hepatocellular carcinoma (HCC), are linked to aggressive...

High-sensitivity photoelectric sensing of apatinib for recurrent hepatocellular carcinoma in TACE.

Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality, with transcatheter arterial chemoembolization (TACE) being a widel...

Deep Learning for Differentiating Pulmonary Metastasis from Primary Lung Cancer Constructed on Frozen Sections for Intraoperative Diagnosis.

The surgical procedures for primary lung cancer and metastatic lung tumors differ. An intraoperative diagnosis is important for surgical procedure dec...