Gastroenterology

Integrated multi-task learning framework for hepatocellular carcinoma segmentation and histological grading using fused multi-phase MRI.

OBJECTIVE: This study aims to develop and validate an integrated multi-task framework for hepatocellular carcinoma analysis by combining deep learning-based segmentation with radiomics-based histological grading using fused multi-phase MRI. MATERIALS AND METHODS: In this retrospective study, MRI data from 1673 patients with histopathologically confirmed hepatocellular carcinoma (875 high-grade, 79...

Machine learning-based integration develops a lactate metabolism related gene signature for improving outcomes in pancreatic ductal adenocarcinoma.

OBJECTIVES: Given its high global mortality rate, pancreatic ductal adenocarcinoma (PDAC) remains a significant area of investigation. However, a robust gene signature linked to lactate metabolism for PDAC patients has not yet been established. Our objective was therefore to construct a novel lactate metabolism related gene signature (LMRGS) capable of predicting patient outcomes and informing the...

Comprehensive multi-omics data to construct hepatocellular carcinoma pathway subtypes and classification model.

The high heterogeneity of Hepatocellular Carcinoma (HCC) severely hampers clinical outcomes. Current classifications based on gene expression profiles...

"PD-PREDICT": A Machine Learning Model for Patient Survival in Peritoneal Dialysis.

Accurate survival prediction in peritoneal dialysis (PD) patients is essential for personalized treatment planning and shared decision-making. We deve...

Long-Term Survival and Beneficiaries of Adjuvant Anti-PD-1 Therapy in Resected Hepatocellular Carcinoma.

BACKGROUND: Early and late hepatocellular carcinoma (HCC) recurrences, which are driven by residual and de novo tumors, respectively, differ in biolog...

Deep Learning-Based Motion-Compensated Reconstruction for Accelerating 4-Dimensional Magnetic Resonance Fingerprinting.

PURPOSE: To develop and validate DeepMocor, a deep learning-based method for motion-compensated 4-dimensional magnetic resonance fingerprinting (4D-MR...

Robotic thoracic surgery: what is different in anesthesia?

PURPOSE OF REVIEW: Robotic-assisted thoracic surgery (RATS) has emerged as a transformative approach in thoracic surgery, enabling enhanced precision ...

Development of a Machine Learning-Based Predictive Model for Anastomotic Leakage Following Gastric Cancer Surgery.

BackgroundThe incidence of anastomotic leakage (AL) following radical gastrectomy for gastric cancer ranges from 2.1% to 14.6%, with mortality rates u...

Physics-informed deep learning sharpens nano diagnostics for elusive pancreatic cancer.

Pancreatic disease affects over 10% of the world population, and the most dangerous is pancreatic cancer (PC). The disease is mostly of late age of on...

Utility of multimodal deep learning model to diagnose lymph node metastasis in esophageal cancer using computed tomography and positron emission tomography images.

PURPOSE: This study aimed to assess the performance of a deep learning model using multimodal imaging for detecting lymph node metastasis in esophagea...

Blood-based Vienna 3P/5P risk models accurately predict first hepatic decompensation in compensated advanced chronic liver disease.

BACKGROUND & AIMS: Invasive measurement of hepatic venous pressure gradient (HVPG) is the gold standard for diagnosing clinically significant portal h...

Improving Colorectal Cancer Screening and Risk Assessment through Predictive Modeling on Medical Images and Records.

Colonoscopy screening effectively identifies and removes polyps before they progress to colorectal cancer (CRC), but current follow-up guidelines rely...

Predictive prioritization of enhancers associated with pancreatic disease risk.

Genetic and epigenetic variation in enhancers is associated with disease susceptibility; however, linking enhancers to target genes and predicting enh...

Neural and behavioral dynamics of dyadic rhythm coordination across limb pairings.

Interpersonal motor synchronization relies on precise neural coordination, yet its underlying brain mechanisms remain incompletely understood. Guided ...

Machine Learning-Enhanced Clinical Decision Support for Diagnosing Sinusitis With Nasal Endoscopy.

BACKGROUND: Sinusitis is a prevalent disease for which nasal endoscopy (NE) is an optimal diagnostic modality. However, NE accuracy is limited by inte...

A rural-to-center artificial intelligence model for diagnosing Helicobacter pylori infection and premalignant gastric conditions using endoscopy images captured in routine practice.

Diagnosing Helicobacter pylori infection and premalignant gastric conditions typically requires 13C urea breath testing or histological assessment, wh...

A prospective study evaluating an artificial intelligence-based system for withdrawal time measurement.

Withdrawal time has emerged as a critical quality measure in colonoscopy for colorectal cancer screening. Owing to the high variability in calculating...

The effect of autonomous mobile robots on improving endoscope reprocessing efficiency: a prospective comparative trial (with video).

BACKGROUND AND AIMS: Autonomous mobile robots (AMRs) have an increasingly wide range of medical applications. However, their use in endoscopy centers ...

From Pixels to Peristalsis: Comparing Achalasia Diagnosis With Artificial Intelligence With Expert Endoscopists.

BACKGROUND & AIMS: Esophageal motility disorders, such as achalasia, require the use of multiple tests to establish a diagnosis. Patients are first as...

Consensus position statements for the standardized application of histological grading and staging systems in MASH clinical trials.

BACKGROUND & AIMS: Assessment of histologic disease activity (grading) and fibrosis (staging) is a prerequisite for patient selection and evaluation o...