Gastroenterology

Pancreatic Diseases

Latest AI and machine learning research in pancreatic diseases for healthcare professionals.

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MAD-UNet: A deep U-shaped network combined with an attention mechanism for pancreas segmentation in CT images.

PURPOSE: Pancreas segmentation is a difficult task because of the high intrapatient variability in t...

Current status of artificial intelligence analysis for endoscopic ultrasonography.

Endoscopic ultrasonography (EUS) is an essential diagnostic tool for various types of pancreatic dis...

Risk factors and socio-economic burden in pancreatic ductal adenocarcinoma operation: a machine learning based analysis.

BACKGROUND: Surgical resection is the major way to cure pancreatic ductal adenocarcinoma (PDAC). How...

Training confounder-free deep learning models for medical applications.

The presence of confounding effects (or biases) is one of the most critical challenges in using deep...

Risk prediction for malignant intraductal papillary mucinous neoplasm of the pancreas: logistic regression versus machine learning.

Most models for predicting malignant pancreatic intraductal papillary mucinous neoplasms were develo...

Intelligent difficulty scoring and assistance system for endoscopic extraction of common bile duct stones based on deep learning: multicenter study.

BACKGROUND: The study aimed to construct an intelligent difficulty scoring and assistance system (DS...

Safety and efficacy of robot-assisted versus open pancreaticoduodenectomy: a meta-analysis of multiple worldwide centers.

The objective of the study is to compare the safety and efficacy of robot-assisted pancreaticoduoden...

Host variables confound gut microbiota studies of human disease.

Low concordance between studies that examine the role of microbiota in human diseases is a pervasive...

Formal robotic training diminishes the learning curve for robotic pancreatoduodenectomy: Implications for new programs in complex robotic surgery.

INTRODUCTION: The learning curve associated with robotic pancreatoduodenectomy (RPD) is a hurdle for...

A deep learning framework for pancreas segmentation with multi-atlas registration and 3D level-set.

In this paper, we propose and validate a deep learning framework that incorporates both multi-atlas ...

The use of machine learning improves the assessment of drug-induced driving behaviour.

RATIONALE: Car-driving performance is negatively affected by the intake of alcohol, tranquillizers, ...

Discrimination of alcohol dependence based on the convolutional neural network.

In this paper, a total of 20 sites of single nucleotide polymorphisms (SNPs) on the serotonin 3 rece...

Predicting alcohol dependence from multi-site brain structural measures.

To identify neuroimaging biomarkers of alcohol dependence (AD) from structural magnetic resonance im...

Deep learning analysis for the detection of pancreatic cancer on endosonographic images: a pilot study.

BACKGROUND/PURPOSE: The application of artificial intelligence to clinical diagnostics using deep le...

Improving the slice interaction of 2.5D CNN for automatic pancreas segmentation.

PURPOSE: Volumetric pancreas segmentation can be used in the diagnosis of pancreatic diseases, the r...

SHIFT: speedy histological-to-immunofluorescent translation of a tumor signature enabled by deep learning.

Spatially-resolved molecular profiling by immunostaining tissue sections is a key feature in cancer ...

Prognostic value of FDG-PET radiomics with machine learning in pancreatic cancer.

Patients with pancreatic cancer have a poor prognosis, therefore identifying particular tumor charac...

Prediction of Type II Diabetes Onset with Computed Tomography and Electronic Medical Records.

Type II diabetes mellitus (T2DM) is a significant public health concern with multiple known risk fac...

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