Gastroenterology

Detection of pathologic complete response using deep neural network-based endoscopic evaluation in patients with esophageal cancer receiving neoadjuvant chemotherapy: a nationwide multicenter retrospective study from 46 Japanese esophageal centers.

BACKGROUND: Detecting pathological complete response (pCR) preoperatively facilitated a non-surgical approach after neoadjuvant chemotherapy (NAC). We previously developed a deep neural network-based endoscopic evaluation to determine pCR preoperatively. Its quality warrants improvement with a larger data series for clinical application.

Machine Learning-Based Radiomics in Malignancy Prediction of Pancreatic Cystic Lesions: Evidence from Cyst Fluid Multi-Omics.

The malignant potential of pancreatic cystic lesions (PCLs) varies dramatically, leading to difficulties when making clinical decisions. This study aimed to develop noninvasive clinical-radiomic models using preoperative CT images to predict the malignant potential of PCLs. It also investigates the biological mechanisms underlying these models. Patients from two retrospective and one prospective c...

Artificial intelligence alert system based on intraluminal view for colonoscopy intubation.

Mucosal contact of the tip of colonoscopy causes red-out views, and more pressure may result in perforation. There is still a lack of quantitative ana...

Gadoxetic acid-enhanced MRI for identifying cholangiocyte phenotype hepatocellular carcinoma by interpretable machine learning: individual application of SHAP.

PURPOSE: Cholangiocyte phenotype hepatocellular carcinoma (HCC) is highly invasive. This study aims to develop and validate an optimal machine learnin...

Analyzing factors influencing hospitalization costs for five common cancers in China using neural network models.

BACKGROUND: Malignant tumors are a major global health crisis, causing 25% of deaths in China, with lung, liver, thyroid, breast, and colon cancers be...

Identifying disease progression biomarkers in metabolic associated steatotic liver disease (MASLD) through weighted gene co-expression network analysis and machine learning.

BACKGROUND: Metabolic Associated Steatotic Liver Disease (MASLD), encompassing conditions simple liver steatosis (MAFL) and metabolic associated steat...

Precision Strike Strategy for Liver Diseases Trilogy with Xiao-Chai-Hu Decoction: A Meta-Analysis with Machine Learning.

BACKGROUND AND PURPOSE: The progression from hepatitis to liver fibrosis (LF) and ultimately to hepatic carcinoma (HCC) represents the advanced stages...

Using deep learning generated CBCT contours for online dose assessment of prostate SABR treatments.

Prostate Stereotactic Ablative Body Radiotherapy (SABR) is an ultra-hypofractionated treatment where small setup errors can lead to higher doses to or...

Advancing Predictive Models for Anastomotic Leakage in Esophageal Cancer Surgery: Opportunities for Enhancing Machine Learning Performance and Clinical Utility.

Mechanism Exploration of Dietary Supplement Astaxanthin on Improving Atherosclerosis through an Integrated Strategy Encompassing Artificial Intelligence Virtual Screening and Experimental Validation.

Atherosclerosis (AS) is a major and common pathological basis of ischemic intestinal infarction, myocardial infarction, stroke, renal failure, and oth...

Bald eagle-optimized transformer networks with temporal-spatial mid-level features for pancreatic tumor classification.

The classification and diagnosis of pancreatic tumors present significant challenges due to their inherent complexity and variability. Traditional met...

Machine learning technique-based four-autoantibody test for early detection of esophageal squamous cell carcinoma: a multicenter, retrospective study with a nested case-control study.

BACKGROUND: Autoantibodies represent promising diagnostic blood-based biomarkers that may be generated prior to the first clinically detectable signs ...

Incorporation of explainable artificial intelligence in ensemble machine learning-driven pancreatic cancer diagnosis.

Despite the strides made in medical science, pancreatic cancer continues to be a threat, highlighting the urgent need for creative strategies to addre...

Evaluating the performance of GPT-3.5, GPT-4, and GPT-4o in the Chinese National Medical Licensing Examination.

This study aims to compare and evaluate the performance of GPT-3.5, GPT-4, and GPT-4o in the 2020 and 2021 Chinese National Medical Licensing Examinat...

Constructing a neural network model based on tumor-infiltrating lymphocytes (TILs) to predict the survival of hepatocellular carcinoma patients.

BACKGROUND: Hepatocellular carcinoma (HCC) is the most common primary liver cancer worldwide, and early pathological diagnosis is crucial for formulat...

AI-based phenotyping of hepatic fiber morphology to inform molecular alterations in metabolic dysfunction-associated steatotic liver disease.

BACKGROUND AND AIMS: Hepatic fiber morphology may significantly enhance our understanding of molecular alterations in metabolic dysfunction-associated...

Nuciferine activates intestinal TAS2R46 to attenuate metabolic disorders and hyperlipidemia via hepatic VLDL regulation.

BACKGROUND: Dysregulated blood lipid metabolism, a primary driver of hyperlipidemia, is closely associated with excessive very low-density lipoprotein...

Recent advances in therapeutic probiotics: insights from human trials.

SUMMARYRecent advances in therapeutic probiotics have shown promising results across various health conditions, reflecting a growing understanding of ...

Artificial intelligence real-time automated recognition of the gastric antrum cross-sectional area and motility rhythm via bedside ultrasound: a pilot study.

The cross-sectional area (CSA) of the gastric antrum and its motility rhythm reflects the gastrointestinal function of critically ill patients. Monito...

Data-driven survival modeling for breast cancer prognostics: A comparative study with machine learning and traditional survival modeling methods.

Background This investigation delves into the potential application of data-driven survival modeling approaches for prognostic assessments of breast c...