Gastroenterology

Risk Prediction of Liver Injury in Pediatric Tuberculosis Treatment: Development of an Automated Machine Learning Model.

PURPOSE: Drug-induced liver injury (DILI) is one of the most common and serious adverse drug reactions related to first-line anti-tuberculosis drugs in pediatric tuberculosis patients. This study aims to develop an automatic machine learning (AutoML) model for predicting the risk of anti-tuberculosis drug-induced liver injury (ATB-DILI) in children.

Opioid growth factor receptor overexpression exerts anti-hepatocellular carcinoma effects by activating P16 and P21 to inhibit proliferation and migration of HepG2 cells.

INTRODUCTION: Hepatocellular carcinoma (HCC) is the sixth most common type of cancer and the second leading cause of cancer death worldwide [19]. Opioid growth factor (OGF) has been shown to exhibit antitumour potential, binding to OGF receptor (OGFr). Naltrexone (NTX), an OGFr antagonist, is considered as a potential anti-cancer agent. However, the specific mechanism of how OGFr acts on HCC cells...

Machine learning based models for predicting presentation delay risk among gastric cancer patients.

OBJECTIVE: Presentation delay of cancer patients prevents the patient from timely diagnosis and treatment leading to poor prognosis. Predicting the ri...

Golgi protein 73: charting new territories in diagnosing significant fibrosis in MASLD: a prospective cross-sectional study.

OBJECTIVES: To explore the correlation between serum Golgi protein 73 (GP73) levels and the degree of fibrosis in Metabolic dysfunction associated ste...

Unsupervised machine learning model for phenogroup-based stratification in acute type A aortic dissection to identify postoperative acute gastrointestinal injury.

OBJECTIVE: We aimed to explore the application value of unsupervised machine learning in identifying acute gastrointestinal injury (AGI) after extraco...

Machine Learning-Assisted High-Throughput Screening of Nanozymes for Ulcerative Colitis.

Ulcerative colitis (UC) is a chronic gastrointestinal inflammatory disorder with rising prevalence. Due to the recurrent and difficult-to-treat nature...

Artificial intelligence in gastrointestinal cancers: Diagnostic, prognostic, and surgical strategies.

GI (Gastrointestinal) malignancies are one of the most common and lethal cancers globally. The dawn of precision medicine and developing technologies ...

A spatiotemporal and machine-learning platform facilitates the manufacturing of hPSC-derived esophageal mucosa.

Human pluripotent stem cell-derived tissue engineering offers great promise for designer cell-based personalized therapeutics, but harnessing such pot...

Multimodal deep-learning model using pre-treatment endoscopic images and clinical information to predict efficacy of neoadjuvant chemotherapy in esophageal squamous cell carcinoma.

BACKGROUND: Neoadjuvant chemotherapy is standard for advanced esophageal squamous cell carcinoma, though often ineffective. Therefore, predicting the ...

Deep Learning for Classification of Inflammatory Bowel Disease Activity in Whole Slide Images of Colonic Histopathology.

Grading activity of inflammatory bowel disease (IBD) using standardized histopathological scoring systems remains challenging due to limited availabil...

Artificial Intelligence for Predicting HER2 Status of Gastric Cancer Based on Whole-Slide Histopathology Images: A Retrospective Multicenter Study.

Human epidermal growth factor receptor 2 (HER2) positive gastric cancer (GC) shows a robust response to the combined therapy based HER2-targeted thera...

FocusUNet: Pioneering dual attention with gated U-Net for colonoscopic polyp segmentation.

The detection and excision of colorectal polyps, precursors to colorectal cancer (CRC), can improve survival rates by up to 90%. Automated polyp segme...

AntiT2DMP-Pred: Leveraging feature fusion and optimization for superior machine learning prediction of type 2 diabetes mellitus.

Pancreatic α-amylase breaks down starch into isomaltose and maltose, which are further hydrolyzed by α-glucosidase in the intestine into monosaccharid...

Postoperative fever after elective minimally invasive resection for gastric and colorectal cancer: incidence, risk factors and characteristics.

PURPOSE: To analyze the incidence and risk factors of postoperative fever (POF) in gastrointestinal cancer (GIC), discuss the influence of POF on shor...

Multi-site, multi-vendor development and validation of a deep learning model for liver stiffness prediction using abdominal biparametric MRI.

BACKGROUND: Chronic liver disease (CLD) is a substantial cause of morbidity and mortality worldwide. Liver stiffness, as measured by MR elastography (...

Multiparametric MRI for Assessment of the Biological Invasiveness and Prognosis of Pancreatic Ductal Adenocarcinoma in the Era of Artificial Intelligence.

Pancreatic ductal adenocarcinoma (PDAC) is the deadliest malignant tumor, with a grim 5-year overall survival rate of about 12%. As its incidence and ...

G-SET-DCL: a guided sequential episodic training with dual contrastive learning approach for colon segmentation.

PURPOSE: This article introduces a novel deep learning approach to substantially improve the accuracy of colon segmentation even with limited data ann...

Integrating traditional machine learning with qPCR validation to identify solid drug targets in pancreatic cancer: a 5-gene signature study.

BACKGROUND: Pancreatic cancer remains one of the deadliest malignancies, largely due to its late diagnosis and lack of effective therapeutic targets.

Characterization of hepatocellular carcinoma with CT with deep learning reconstruction compared with iterative reconstruction and 3-Tesla MRI.

OBJECTIVES: This study compared the characteristics of lesions suspicious for hepatocellular carcinoma (HCC) and their LI-RADS classifications in adap...

The Use of Artificial Intelligence for Endoscopic Evaluation of the Small Bowel.

There remains great potential for widespread implementation of artificial intelligence (AI) in managing small bowel disorders. Studies have shown exce...