Gastroenterology

Latest AI and machine learning research in gastroenterology for healthcare professionals.

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Generative AI in Pediatric Gastroenterology.

PURPOSE OF REVIEW: The integration of digital technology into medical practice is often thrust upon ...

Metabolic phenotyping combined with transcriptomics metadata fortifies the diagnosis of early-stage Hepatocellular carcinoma.

INTRODUCTION: The low sensitivity of alpha-fetoprotein (AFP) renders it unsuitable as a stand-alone ...

Imatinib adherence prediction using machine learning approach in patients with gastrointestinal stromal tumor.

BACKGROUND: Nonadherence to imatinib is common in patients with gastrointestinal stromal tumor (GIST...

Deep Learning Based Shear Wave Detection and Segmentation Tool for Use in Point-of-Care for Chronic Liver Disease Assessments.

OBJECTIVE: As metabolic dysfunction-associated steatotic liver disease (MASLD) becomes more prevalen...

Molecular designing of potential environmentally friendly PFAS based on deep learning and generative models.

Perfluoroalkyl and polyfluoroalkyl substances (PFAS) are widely used across a spectrum of industrial...

Building Machine Learning Models in Gastrointestinal Endoscopy.

The current landscape of machine learning models in GI endoscopy is fraught with considerable variab...

Data privacy-aware machine learning approach in pancreatic cancer diagnosis.

PROBLEM: Pancreatic ductal adenocarcinoma (PDAC) is considered a highly lethal cancer due to its adv...

Development of a Diagnostic Model for Pancreatic Ductal Adenocarcinoma Using Machine Learning and Blood-Based miRNAs.

INTRODUCTION: Pancreatic ductal adenocarcinoma (PDAC) has the lowest survival rate among all major c...

Harnessing AI for precision tonsillitis diagnosis: a revolutionary approach in endoscopic analysis.

BACKGROUND: Diagnosing and treating tonsillitis pose no significant challenge for otolaryngologists;...

Exploring the benefits and challenges of AI-driven large language models in gastroenterology: Think out of the box.

Artificial Intelligence (AI) has evolved significantly over the past decades, from its early concept...

Artificial intelligence for ultrasonographic detection and diagnosis of hepatocellular carcinoma and cholangiocarcinoma.

The effectiveness of ultrasonography (USG) in liver cancer screening is partly constrained by the op...

Deep Generative Adversarial Reinforcement Learning for Semi-Supervised Segmentation of Low-Contrast and Small Objects in Medical Images.

Deep reinforcement learning (DRL) has demonstrated impressive performance in medical image segmentat...

Exploiting histopathological imaging for early detection of lung and colon cancer via ensemble deep learning model.

Cancer seems to have a vast number of deaths due to its heterogeneity, aggressiveness, and significa...

Deep neural networks integrating genomics and histopathological images for predicting stages and survival time-to-event in colon cancer.

MOTIVATION: There exists an unexplained diverse variation within the predefined colon cancer stages ...

Serum targeted metabolomics uncovering specific amino acid signature for diagnosis of intrahepatic cholangiocarcinoma.

Intrahepatic cholangiocarcinoma (iCCA) is a hepatobiliary malignancy which accounts for approximatel...

Performance of explainable artificial intelligence in guiding the management of patients with a pancreatic cyst.

BACKGROUND/OBJECTIVES: Pancreatic cyst management can be distilled into three separate pathways - di...

A machine learning model to predict liver-related outcomes after the functional cure of chronic hepatitis B.

BACKGROUND & AIMS: The risk of hepatocellular carcinoma (HCC) and hepatic decompensation persists af...

A Deep Learning Approach for the Identification of the Molecular Subtypes of Pancreatic Ductal Adenocarcinoma Based on Whole Slide Pathology Images.

Delayed diagnosis and treatment resistance result in high pancreatic ductal adenocarcinoma (PDAC) mo...

Inflammatory bowel disease genomics, transcriptomics, proteomics and metagenomics meet artificial intelligence.

Various extrinsic and intrinsic factors such as drug exposures, antibiotic treatments, smoking, life...

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