Peptic Ulcer Disease

Predicting the magnitude of risk for non-curative endoscopic submucosal dissection in superficial esophageal cancer using explainable artificial intelligence.

BACKGROUND: Endoscopic submucosal dissection (ESD) serves as a critical treatment modality for superficial esophageal cancer. However, non-curative resection is significantly associated with residual tumors and unfavorable prognosis. Effective preoperative predictive tools are currently lacking. AIM: To develop and validate a machine learning-based prediction model for accurate preoperative assess...

Deep learning in lower gastrointestinal cancer detection: Advances in endoscopic, radiologic, and histopathologic diagnostics.

Gastrointestinal (GI) cancers, particularly colorectal cancer, continue to be a major contributor to global cancer-related morbidity and mortality. Despite significant advancements in screening protocols and treatment strategies, early detection remains a clinical challenge due to the limitations of conventional diagnostic tools, which often suffer from inter-observer variability, limited sensitiv...

The application of Machine learning in predicting the outcomes of minimally invasive treatments for uterine Fibroids: A systematic review and meta-analysis.

RATIONALE AND OBJECTIVES: Uterine fibroids (UFs) are common benign tumors that impact women's health, particularly through symptoms such as abnormal b...

PlantCTCIP: Chromatin Interaction Prediction Using Convolutional Neural Network and Transformer in Plants.

Chromatin interactions establish spatial proximity between distant regulatory elements and their target genes, significantly influencing gene expressi...

Outcomes of Artificial Intelligence Enhanced Colonoscopy in a Tertiary Clinical Setting.

BACKGROUND AND AIMS: Colonoscopies reduce colorectal cancer incidence and mortality, but challenges remain in detecting all precancerous lesions. Oper...

Development and evaluation of a prediction model for adult ICU hemorrhage using only continuous cardiorespiratory data.

Develop and evaluate whether a model trained to detect the physiological signature of hemorrhage in ICU patients generalizes to other cohorts. App...

[Application of a novel robot-assisted navigation system in CT-guided percutaneous lung biopsy].

Objective: To compare the clinical efficacy and safety of robot-assisted navigation systems with those of the conventional puncture localization metho...

Machine learning for hemodynamic instability prediction and hemorrhage management in trauma and perioperative care.

PURPOSE OF REVIEW: Hemodynamic instability and uncontrolled hemorrhage remain leading causes of preventable morbidity and mortality in trauma and peri...

Establishment of insulin resistance-related ten-gene signature in endometrial cancer and identification of ACTL8 as a prognostic and immunological biomarker.

BACKGROUND: Endometrial cancer (EC) is a common gynecological tumor. Insulin resistance (IR) increases the risk of EC. However, the common molecular b...

Infectious disease diagnosis by artificial intelligence (AI): Differences in patient backgrounds and symptoms between antigen test positives and novel AI-powered pharyngeal endoscopy test positives.

This study aimed to identify differences in patient background and symptoms between individuals who tested positive using conventional rapid antigen (...

Efficacy of artificial intelligence-assisted upper gastrointestinal endoscopy for neoplasm detection: a systematic review and meta-analysis of randomized controlled trials.

BACKGROUND AND AIMS: EGD remains the primary diagnostic modality for evaluating lesions of the upper gastrointestinal (UGI) tract. This meta-analysis ...

Detection of esophageal varices and prediction of hepatic decompensation in unresectable hepatocellular carcinoma using AI.

BACKGROUND & AIMS: In hepatocellular carcinoma (HCC) with cirrhosis, portal hypertension worsens outcomes. Esophagogastroduodenoscopy (EGD), the curre...

Identification of mitochondrial dysfunction-related biomarkers and immune infiltration in liver ischemia-reperfusion injury via integrated bioinformatics and machine learning.

BACKGROUND: Mitochondrial dysfunction contributes to the pathogenesis of multiple diseases. This study explores the involvement of mitochondrial dysfu...

Artificial Intelligence for Gastroenterology Practice: A Modified Delphi Consensus.

BACKGROUND: The American College of Gastroenterology (ACG) assembled a multidisciplinary task force to evaluate the current state and future direction...

Development and validation of a prognosis model for low-grade gliomas based on metabolic gene risk scoring and immune microenvironment interaction.

PURPOSE: Low-grade gliomas(LGGs) show significant clinical and molecular heterogeneity, complicating progression prediction with conventional indicato...

HLA-DPA1 as a diagnostic biomarker differentiating early- and late-onset preeclampsia.

The occurrence and development of a wide range of preeclampsia (PE), especially early-onset preeclampsia (EOPE), is closely associated with the immune...

XL-MSDigger: a deep learning-based, versatile solution for cross-linking mass spectrometry.

Cross-linking mass spectrometry (XL-MS) technology plays an important role in protein structure and protein-protein interaction (PPI) studies. However...

Machine Learning Model Development and Validation in Individuals With Suggestive Hypersensitivity to Nonsteroidal Anti-inflammatory Drugs.

BACKGROUND AND OBJECTIVES: Nonsteroidal anti-inflammatory drugs (NSAIDs) are the main triggers of drug hypersensitivity reactions (HSRs). NSAID-induce...

Early individualized risk prediction using clinical data for children during the febrile phase of dengue in outpatient settings in Vietnam and Thailand.

Dengue severity prediction models are usually developed using hospitalized patient data, but triage and hospital admission are mainly evaluated in out...

MAGIN-GO: Protein function prediction based on dual graph neural networks and gene ontology structure.

Proteins are fundamental to the execution of biological activities, and the accurate prediction of their functions is of paramount importance for prot...