Gastroenterology

Deep in the Bowel: Highly Interpretable Neural Encoder-Decoder Networks Predict Gut Metabolites from Gut Microbiome.

BACKGROUND: Technological advances in next-generation sequencing (NGS) and chromatographic assays [e...

Detection and genomic characterization of hepatitis E virus genotype 3 from pigs in Ghana, Africa.

BACKGROUND: Hepatitis E virus (HEV) is a major cause of human hepatitis worldwide. Zoonotic genotype...

Development and validation of an artificial neural network prognostic model after gastrectomy for gastric carcinoma: An international multicenter cohort study.

BACKGROUND: Recently, artificial neural network (ANN) methods have also been adopted to deal with th...

Effect of Missing Data Imputation on Deep Learning Prediction Performance for Vesicoureteral Reflux and Recurrent Urinary Tract Infection Clinical Study.

Missing observations are always a challenging problem that we have to deal with in diseases that req...

Added value of 3D-vision during robotic pancreatoduodenectomy anastomoses in biotissue (LAEBOT 3D2D): a randomized controlled cross-over trial.

BACKGROUND: We tested the added value of 3D-vision on procedure time and surgical performance during...

Deep-learning-based accurate hepatic steatosis quantification for histological assessment of liver biopsies.

Hepatic steatosis droplet quantification with histology biopsies has high clinical significance for ...

Polyp Segmentation with Fully Convolutional Deep Neural Networks-Extended Evaluation Study.

Analysis of colonoscopy images plays a significant role in early detection of colorectal cancer. Aut...

Deep convolutional neural network-based anomaly detection for organ classification in gastric X-ray examination.

AIM: The aim of this study was to determine whether our deep convolutional neural network-based anom...

Molecular docking and machine learning analysis of Abemaciclib in colon cancer.

BACKGROUND: The main challenge in cancer research is the identification of different omic variables ...

Improving the accuracy of gastrointestinal neuroendocrine tumor grading with deep learning.

The Ki-67 index is an established prognostic factor in gastrointestinal neuroendocrine tumors (GI-NE...

Radiomics in liver diseases: Current progress and future opportunities.

Liver diseases, a wide spectrum of pathologies from inflammation to neoplasm, have become an increas...

Detecting rare diseases in electronic health records using machine learning and knowledge engineering: Case study of acute hepatic porphyria.

BACKGROUND: With the growing adoption of the electronic health record (EHR) worldwide over the last ...

How can endoscopists adapt and collaborate with artificial intelligence for early gastric cancer detection?

Finding undiagnosed patients with hepatitis C infection: an application of artificial intelligence to patient claims data.

Hepatitis C virus (HCV) remains a significant public health challenge with approximately half of the...

Artificial intelligence for the detection of esophageal and esophagogastric junctional adenocarcinoma.

BACKGROUND AND AIM: Conventional endoscopy for the early detection of esophageal and esophagogastric...

Hypothesis-free deep survival learning applied to the tumour microenvironment in gastric cancer.

The biological complexity reflected in histology images requires advanced approaches for unbiased pr...

Automatic task recognition in a flexible endoscopy benchtop trainer with semi-supervised learning.

PURPOSE: Inexpensive benchtop training systems offer significant advantages to meet the increasing d...

Robot-assisted combined pancreatectomy/hepatectomy for metastatic pancreatic acinar cell carcinoma: case report and review of the literature.

Acinar cell carcinoma (ACC) of the pancreas is a rare neoplasm with less aggressive behavior than du...

What is the optimal surveillance strategy for non-dysplastic Barrett's esophagus?

PURPOSE OF REVIEW: There is conflicting data on the effectiveness of the currently recommended endos...

A machine learning-based prognostic predictor for stage III colon cancer.

Limited biomarkers have been identified as prognostic predictors for stage III colon cancer. To comb...

Automatic segmentation of pelvic organs-at-risk using a fusion network model based on limited training samples.

Efficient and accurate methods are needed to automatically segmenting organs-at-risk (OAR) to accel...